RU2387079C2 - Diversity of directional pattern for supporting receiver of multiple-input and multiple-output (mimo) system and corresponding methods - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: receiver is connected to the receiving side of an antenna array and consists of N antenna elements for receiving at least M different sums from M source signals, where N is less than M. A signal division processor is connected to the receiver for generating a mixing matrix which contains at least M different sums from M source signals such that the mixing matrix has a rank at least equal to M. The signal division processor separates useful source signals from the mixing matrix.

EFFECT: reduced number of antenna elements in the receiver of a multiple-input and multiple-output system compared to the number of antenna elements in a MIMO system transmitter and retention of reliability of a MIMO communication system.

24 cl, 6 dwg

Description

FIELD OF THE INVENTION

The present invention relates to the field of communication systems and, in particular, to a receiver of a system with multiple inputs and multiple outputs (MIMO), operating with a compact antenna array.

State of the art

A multi-input multiple output (MIMO) wireless communication system includes multiple antenna elements in a transmitter and multiple antenna elements in a receiver. The corresponding antenna array is formed in the transmitter and in the receiver based on the corresponding antenna elements.

Antenna elements are used in such an environment with multipath propagation of signals, that is, due to the presence of various scattering objects of scattering in the medium, each signal experiences multipath propagation. The receiving antenna elements receive the transmitted signals, and then the signal processing method is applied to separate the transmitted signals and restore user data.

The signal processing method may be a blind source separation process (BSS). Separation is “blind” because it is often performed with limited information about the transmitted signals, the sources of the transmitted signals, and the effects that the propagation channel exerts on the transmitted signals. The three commonly used blind signal separation techniques are basic component analysis (PCA), independent component analysis (ICA), and singular value decomposition (SVD).

Multiple Input Multiple Output (MIMO) communication systems have the advantage of being able to improve the throughput of a wireless link between a transmitter and a receiver. An environment with multipath propagation of signals makes it possible to form several orthogonal channels between them. Then, data for an individual user can be simultaneously transmitted wirelessly over these channels simultaneously and using the same bandwidth.

Modern communication systems with multiple inputs and multiple outputs (MIMO) use antenna elements with spatial diversity so that the number of orthogonal channels that can be formed is not reduced. The problem with this implementation is that the performance of a multi-input multi-output (MIMO) communication system is usually proportional to the number of antenna elements used.

An increase in the number of antenna elements increases the size of the antenna arrays for communication systems with multiple inputs and multiple outputs (MIMO). When a receiver with multiple inputs and multiple outputs (MIMO) is implemented in a small portable communication device, there is a small available volume for a large number of antenna elements, and installing antenna elements outside the communication device is a problem for the user.

One approach to providing a more compact antenna array for a multi-input multi-output (MIMO) receiver is disclosed in US Pat. No. 6,870,515. Instead of using spatial diversity antenna elements, polarization diversity is used. Since antenna elements located close in space are used, this will allow for the implementation of a compact antenna array for a receiver with multiple inputs and multiple outputs (MIMO).

Even though a more compact antenna array is provided, the performance of a multi-input multi-output (MIMO) communication system is still based on the number of antenna elements in the receiver, which is equal to or greater than the number of antenna elements in the transmitter. For example, the '515 patent discloses that the number of receiving antenna elements is equal to or greater than the number of transmitting antenna elements.

SUMMARY OF THE INVENTION

Based on the foregoing description of the prior art, an object of the present invention is to reduce the number of antenna elements in a receiver of a multi-input and multiple output (MIMO) system compared to the number of antenna elements in a transmitter of a MIMO system, while still providing a reliable MIMO communication system.

This and other objectives, features and advantages in accordance with the present invention are provided by means of a MIMO communication system comprising a transmitter and a transmitting antenna array connected to the transmitter and containing M antenna elements for transmitting M source signals.

On the receiving side, the receiving antenna array is connected to the receiver and contains N antenna elements for receiving at least M different sums of M source signals, where N is less than M. A signal separation processor can be connected to the receiver to form a mixing matrix containing at least M different sums of M source signals, so that the mixing matrix has a rank of at least M. The signal separation processor extracts useful source signals from the mixing matrix.

The signal separation processor may be a blind signal separation processor. A blind signal separation processor may extract useful source signals from a mixing matrix based on at least one of the main component analysis (PCA), independent component analysis (ICA), and singular decomposition (SVD) methods.

Alternatively, the signal separation processor may extract useful source signals from the mixing matrix based on the knowledge extraction process. The knowledge-based signal extraction process can extract useful source signals from the mixing matrix based on at least one of the process of nulling negligible coefficients (ZF) and the minimum mean square estimation (MMSE) process.

The receiving antenna array preferably receives M different sums from the M (signals) of the source using N antenna elements, with N <M. N antenna elements form at least M different antenna patterns for receiving M different sums from M source signals. M different sums of the M source signals received by the N antenna elements in the receiver array are used to populate the mixing matrix so that the mixing matrix has a rank of at least M.

The rank of the mixing matrix determines how many signals can actually be extracted. The higher the rank, the more signals can be highlighted. Therefore, a compact antenna array having N antenna elements that are smaller than the M antenna elements in the transmitting antenna array can be used by the receiver of the MIMO system, while still providing a reliable communication system with multiple inputs and multiple outputs (MIMO).

There are several different embodiments of the receiving antenna array. N antenna elements can be correlated to form a phased array. In another embodiment, the N correlated antenna elements may comprise at least one active antenna element and up to N-1 passive antenna elements to form a switched beam antenna. In addition, at least two of the N correlated antenna elements may have different polarizations.

Other embodiments of a receiving antenna array may have a multiplication effect on received M different sums from M source signals. This successfully allows you to further increase the rank of the mixing matrix without the need to increase the number N of antenna elements in the receiving antenna array.

By increasing the rank of the mixing matrix, blind signals can be extracted with more signals.

The multiplication effect for the number of received M different sums from the M source signals can be achieved using one of the following methods or a combination thereof. Deviation of the grating leads to a change in the slope of the antenna pattern to receive additional sums of source signals. The path selection can be made so that all the sums of the source signals used to fill the mixing matrix are correlated and / or statistically independent. Signal separation can also be used to further populate the mixing matrix. Various summed signals may be separated using spreading codes, or they may be divided into common mode (I) and quadrature (Q) components.

Although M linearly independent sums are a necessary minimum to support the full implementation of M elements of a MIMO transmit antenna, there are advantages to exceed M. For example, not all N antenna elements in a receiving antenna array can be oriented to receive M linearly independent sums. Similarly, not all accepted amounts are linearly independent enough.

In addition, if there are sources of interference or noise, an additional rank of the mixing matrix may be required to isolate these signals. Another advantage of isolating sources of interference or noise is the resulting reduction in the signal-to-noise ratio, which enables higher data rates, lower error rates and / or reduced transmission power.

For the two reasons mentioned above, there is an advantage in increasing the rank of the mixing matrix above M, which is related to the number of usable channels. This increase of L additional amounts in addition to the usual M can provide a more reliable implementation of the MIMO system. Depending on the means available to increase the mixing matrix, the number of elements of the receiving antenna may still be less than the classical quantity M for MIMO systems or may be increased to M or more to enable the rank of the mixing matrix to be increased to N + L> M.

Another aspect of the invention is directed to a method for controlling a MIMO communication system described above.

Brief Description of the Drawings

Figure 1 is a block diagram of a communication system with multiple inputs and multiple outputs MIMO in accordance with the present invention.

Figure 2 is a more detailed block diagram of the elements on the receiving side of the MIMO communication system shown in figure 1.

FIG. 3 is a block diagram of a receiver of a MIMO system based on grating deflection to provide various signal sums for blind signal processing in accordance with the present invention.

FIG. 4 is a block diagram of a receiver of a MIMO system operating on the basis of path selection to provide various signal sums for blind signal processing in accordance with the present invention.

5 is a block diagram of a receiver of a MIMO system operating on the basis of spreading codes to provide additional signal sums for blind signal processing in accordance with the present invention.

6 is a block diagram of a receiver of a MIMO system operating on the basis of the in-phase and quadrature components of the signal to provide additional signal sums for processing with blind separation of signals, in accordance with the present invention.

Detailed Description of Preferred Embodiments

The present invention will now be described more fully with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. However, this invention can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are presented so that this disclosure is complete and complete and fully conveys the scope of the invention for specialists in the field of technology. Similar numbers everywhere refer to similar elements.

Now, with reference to FIG. 1, a MIMO communication system 20 will be described. The MIMO communication system 20 includes a transmitter 30, a transmitting antenna array 32, a receiver 40, and a receiving antenna array 42. It will be readily apparent to those skilled in the art that the transmitter 30 and receiver 40 can be replaced by transceivers. Therefore, their respective antenna arrays 32, 42 support two-way data exchange. However, for purposes of illustrating the present invention, reference will be made to a transmitter 30 and a receiver 40.

The transmit antenna array 32 includes M antenna elements 33 (1) -33 (M) for transmitting M source signals 34 (1) -34 (M). Antenna elements 33 (1) -33 (M) can, for example, be spatially correlated. The source signals 34 (1) -34 (M) can be generally indicated at 34, and the antenna elements 33 (1) -33 (M) can be generally indicated at 33.

The receiver antenna array 42 includes N antenna elements 43 (1) -43 (N) for receiving at least M different sums of M source signals, where N is less than M. Since N <M, a compact antenna array can be used in receiver 40, while a reliable MIMO communication system 20 can still be obtained, as will be discussed in more detail below. Antenna elements 43 (1) -43 (N) may be generally indicated at 43.

Corresponding antenna arrays 32, 42 are used in an environment with multipath propagation of signals in such a way that, due to the presence of various scattering objects (buildings, cars, elevations, etc.) in the environment, each signal experiences multipath propagation. Each path can be considered as a separate communication channel. Thus, reference numeral 50 in FIG. 1 represents a scattering medium creating a plurality of channels between the transmitting and receiving antenna arrays 32, 42. Data is transmitted from the transmitting antenna array 32 using a space-time coding (STC) transmission method known in the art .

In addition to the M source signals in the scattering medium 50, there may be L signals 35 of the interference source from the interference source 37 and interfere with the separation of the useful source signals. Various means for increasing the mixing matrix can be successfully used to populate the mixing matrix outside of rank M, as will be discussed in more detail below.

A receive antenna array 42 captures M different sums from the M signals of the source 34, and then a signal processing technique is used to separate the signals. A blind signal separation processor (BSS) 44 is coupled to a receiver 40 to form a mixing matrix 46 containing at least M different sums M from the source signals, so that the mixing matrix has a rank of at least M. The processor 44 blind signal separation extracts the useful source signals from the mixing matrix 46.

As discussed in more detail in the parent patent application, the three commonly used methods that are part of the blind signal separation group are basic component analysis (PCA), independent component analysis (ICA), and singular decomposition (SVD). As long as the signals are independent in some measurable characteristic, and if their sums of signals are linearly independent of one another, one or more of these blind signal separation techniques can be used to extract independent or useful source signals from a mixture of source signals. The measured characteristic is often some combination of the first, second, third or fourth moments of the signals.

The PCA method “whitens” the signals, uses the first and second moments, and rotates the data set based on the correlation properties. If the signal-to-noise ratios of the source signals are high, the signal separation process may end with the PCA method.

If the signal-to-noise ratios of the source signals are low, then the ICA method extracts the source signals based on statistical attributes containing the third and fourth moments of the source signals. Some source signals are Gaussian, and their third and fourth moments depend on the first and second moments. The random noise source may be Gaussian, and the spread spectrum signals are designed to appear Gaussian to the decoders by means of a spreading code different from their own. Under given conditions, the set of signals may appear Gaussian due to the central limit theorem. The ICA approach can single out a single Gaussian signal. As an alternative to the ICA method and the PCA method, the SVD method extracts source signals from a mixture of source signals based on their eigenvalues.

As an alternative to the blind signal separation processor, a signal separation processor may be used to extract useful source signals from the mixing matrix using a knowledge-based signal extraction process. The knowledge-based signal separation process extracts useful source signals from the mixing matrix based on at least one of the process of nulling negligible coefficients (ZF) and the minimum mean square estimation (MMSE) process.

Now, with reference to FIG. 2, various elements on the receiving side of the MIMO communication system 20 will be discussed in more detail. The receiving antenna array 42 includes N antenna elements 43 (1) -43 (N) for receiving at least M different sums of M source signals 34, where N and M are greater than 1 and N is less than M. The receiving antenna array 42 not limited to any specific configuration. The receiving antenna array 42 may include one or more antenna elements 43. The antenna elements 43 may be configured such that the antenna array forms, for example, a phased array or a switched beam antenna.

To construct the mixing matrix 46, the goal is to create various sums of signals. In this application, useful signals can actually always be lower than sources of interference, and still be separated. Because of this significant difference in target, the distances between the antenna elements 43 do not have to correspond to a predetermined spacing, as is usually required for active and passive beam-forming antenna arrays.

The receiver 40 is connected in a downward direction to a receiving antenna array 42 for receiving at least M different sums of M source signals 34. Blind signal separation processor 44 is in a downward direction from receiver 40. Although processor 44 is illustrated separately from receiver 40, the processor may also be included in the receiver. Various sums of M source signals 34 received by receiver 40 are used to populate the mixing matrix 46. Then, the mixing matrix 46 is processed by one or more blind separation processing modules 62, 64 and 66 in the processor 60.

Blind split signal processing modules include PCA module 62, ICA module 64, and SVD module 66. These modules 62, 64, and 66 may be implemented as part of a blind separation processor 44. The PCA module 62 operates on the basis of the first and second moments of the various sums of the received source signals, while the ICA module 64 operates on the basis of the third and fourth moments of the same signals. SVD module 66 performs signal separation based on eigenvalues of various sums of received source signals.

The correlation processing initially performed by the PCA module 62 determines an initial separation matrix 68 (1) for various sums of source signals, and then the ICA module 64 determines an improved separation matrix 68 (2) for separating the source signals in the mixing matrix 46. If the signals are separated by SVD module 66, a separation matrix 68 (3) is also determined to separate the various sums of the received source signals in the mixing matrix 46.

The signals extracted from each respective separation matrix 68 (1) -68 (3) are represented by 49. The extracted signals 49 are then analyzed by the signal analysis module 70 to determine which signals are of interest and which signals are sources of interference . The application dependent processing module 72 processes the signals output from the signal analysis module 70.

The choice as to which signals are of interest may not always imply the final signal to be decoded. For example, an application may require identifying sources of interference and subtracting them from the various sums of the received source signals and then applying the reduced signal to the wave decoder. In this case, the signals of interest are those that are ultimately rejected.

The rank of the mixing matrix 46 determines how many signals can actually be extracted. For example, a mixing matrix having a rank of 4 implies that 4 source signals can be extracted. Ideally, the rank of the mixing matrix 46 should be equal to at least the number M of signal sources. The higher the rank, the more signals that can be highlighted. As the number of M sources increases, the required number of N antenna elements also increases. The '515 patent, discussed in the prior art section, discloses that the number N of antenna elements in a receiver is equal to or greater than the number M of antenna elements in a transmitter, i.e., N≥M.

The receiving antenna array 42 successfully receives M different sums from M source signals 34 using N antenna elements 33, wherein N <M. N antenna elements 43 form at least M different antenna patterns for receiving M different sums from M source signals. M different sums of the M source signals 34 received by the N antenna elements 43 in the receiver array 42 are used to populate the mixing matrix 46 so that the mixing matrix has a rank of at least M.

As noted above, the rank of the mixing matrix 46 determines how many signals can actually be extracted. The higher the rank, the more signals can be highlighted. Therefore, a compact receiving antenna array 42 having N antenna elements 43, which is smaller than the M antenna elements 33 in the transmitting antenna array 32, can be used by the receiver 40 of the MIMO system, while still providing a reliable MIMO communication system 20.

Although M linearly independent sums are a necessary minimum to support the full implementation of MIMO for M transmit antenna elements 34, there are advantages to exceed M. For example, not all N antenna elements 43 in receive antenna array 42 may be oriented to receive M linearly independent sums. Similarly, not all accepted amounts are linearly independent enough. There may also be L other signals that degrade the signal-to-noise ratio in addition to the M known shared signal streams.

Therefore, it is advantageous to take advantage of increasing the rank of the mixing matrix to M + L whenever possible. Another advantage of isolating sources of interference or noise is the resulting reduction in the signal-to-noise ratio, which enables higher data rates, lower error rates and / or reduced transmission power.

For example, L interference signal signals 35 may exist and interfere with the separation of useful source signals 34 from the mixing matrix, where L is greater than 1. If the increase in the rank of the mixing matrix has been exhausted without the need for adding additional antenna elements, then adding at least one additional the antenna element will provide additional means for increasing the rank of the mixing matrix. Adding additional elements can still leave the number of elements less than M for the classical MIMO approach, or it can increase the number of elements to M or even increase it and make more M. Depending on the winnings obtained by increasing the rank of the mixing matrix, it can still be appropriate, even if this increases the number of antenna elements of the receiver. For example, a mix matrix of rank M + L, requiring M elements, will often be a better implementation compared to an implementation with M elements using a classic receiver with MIMO processing. However, in order to illustrate the present invention, the following discussion will focus on M source signals.

There are several different embodiments of the receiving antenna array 42. N antenna elements 43 may be correlated to form a phased array. In another embodiment, the N correlated antenna elements 43 may comprise at least one active antenna element and up to N-1 passive antenna elements for forming a switched beam antenna. In addition, at least two of the N correlated antenna elements may have different polarizations.

Other embodiments of the receiving antenna array 42 may have a multiplication effect on received M different sums from M source signals. This successfully allows one to further increase the rank of the mixing matrix 46 without having to increase the number N of antenna elements 43 in the receiving antenna array 42. By increasing the rank of the mixing matrix 46, more signals can be extracted by the blind signal separation processor 44.

The multiplication effect for the number of received M different sums from M source signals 34 can be achieved using one or a combination of the following methods. The deviation of the lattice leads to a change in the slope of the antenna pattern to receive additional sums of signals 34 of the source. The path selection can be made so that all the sums of the source signals 34 used to populate the mixing matrix 46 are correlated and / or statistically independent. Signal separation can also be used to further populate the mixing matrix 46. Various summed signals may be separated using spreading codes, or they may be divided into common mode (I) and quadrature (Q) components.

Now, with reference to FIGS. 3-6, various embodiments of the receiving antenna array will be discussed in more detail. With reference to FIG. 3, deviation of the antenna array will be considered. The receiving antenna array 142 comprises N antenna elements 143 for generating N initial antenna patterns for receiving N different sums from M source signals. The receiver antenna array 142 also includes a tilt controller 141 for selectively tilting at least one of the N initial antenna patterns to form at least one additional antenna pattern so that at least one is received one additional other sum of M source signals.

The receiver 140 is connected to the receiving antenna array 142 and receives N different sums of M source signals using N initial antenna patterns, and also receives at least one additional other sum of M source signals using at least one additional radiation patterns of the antenna.

Blind signal separation processor 144 is coupled to receiver 140 to form a mixing matrix 146 containing N different sums of M source signals and at least one additional other sum of M source signals. The mixing matrix has a rank of N plus the number of additional different sums of M source signals received using additional antenna patterns. The resulting rank of the mixing matrix 146 is at least M. The processor 144 extracts useful signals from the mixing matrix 146.

In general, any antenna array means that provides sums of signals suitable to increase the rank of the mixing matrix can be used with a deflection mechanism. The deviation forms two different and suitable for use with the mixing matrix sums of signals for each of the means of the antenna array. Therefore, by using this method, there is the effect of multiplying by 2.

If the deviation of the antenna array is segmented into K different regions associated with the antenna, each of the K regions can provide 2 independent regions of deviation and recording in the mixing matrix. For example, if the antenna array can provide N sums per se and there are K different deviation regions, the number of sums of signals in the mixing matrix can be 2NK.

Separation of source signals provided by M signal sources based on path selection will be discussed with reference to FIG. 4. The receiving antenna array 242 contains N elements 243 for generating at least N antenna beams for receiving at least N different sums of M source signals, where N and M are greater than 2.

A controller 250 is coupled to the antenna array 242 to selectively form at least N antenna beams. A receiver unit 240 is coupled to the antenna array 242 to receive at least N different sums of M source signals. Blind signal separation processor 244 is coupled to a receiver unit to form a mixing matrix 246 containing at least up to N different sums of M source signals.

The signal separation processor 244 also determines whether the different sums of the M source signals are correlated or statistically independent, and if not, then interacts with the controller 250 to generate different beams to receive different new sums of the M source signals to replace the different sums of M signals sources that are not correlated or statistically independent in the mixing matrix 246. As a result, at least M different sums of the source signals are received so that the mixing matrix has a rank of at least M. Then, the useful source signals are extracted from the mixing matrix 246.

A multi-channel (rake) receiver is a radio signal receiver designed to counteract the effects of multipath fading. This is realized through the use of several independent receivers, each of which has a small delay for tuning to the individual component of multipath propagation. It can be used by most types of radio access networks. It has proven to be particularly beneficial for modulation types with extension codes. Its ability to select specific signal paths makes it suitable as a means to change the paths supplied to the blind signal separation processor 244.

The selective formation of N beams of the antenna, discussed above, can be applied to all wireless access networks, as experts in the field of technology will easily understand. For code division multiple access (CDMA) systems, the receiver unit 240 contains N multi-channel receivers 256. Each multi-channel receiver 256 contains k rake channels for selecting k different multipath components for each of N different sums from M source signals, received by the corresponding antenna element connected to them. In this configuration, blind signal separation processor 244 is coupled to N multi-channel receivers 256 to form a mixing matrix 246. The mixing matrix 246 contains at least up to kN different multipath components of at least N different sums of M source signals, and the mixing matrix has a rank that is up to kN, where kN is at least M.

In particular, when CDMA signals propagate, they often experience many propagation paths from source to destination. The multi-channel receiver 256 is specifically designed to receive several of these individual instances and combine them for more reliable decoding of signals. While the original signal propagates along each path, its properties change in accordance with the unique characteristics of the propagation path. Under some circumstances, the modification of the correlation and / or statistical properties of the received signal will be large enough to be considered as shared signal streams. The modified multi-channel receiver 256 can be used to extract each changed stream and supply it as a unique input to the mixing matrix 246. Although this ranking tool will not always be available, it will usually be available in high multipath environments when it is most likely to be needed.

While the multi-channel receiver 256 may use different propagation paths, a more general approach applicable to any modulation technique is beamforming. It differs from the multi-channel receiver 256, since beamforming is used to improve the useful signal, as well as to reject the useful signal. However, the difference is that the rejected signal may actually be a different version of the signal intended for the receiver. However, receiver unit 240 needs to detect several of these unique versions of the propagation path of the same signal in order to build the mixing matrix 246 to a sufficient rank.

Signal separation is also used to further populate the mixing matrix A. In one approach, the summed signals are separated using spreading codes. In another approach, the summation signals are separated using in-phase (I) and quadrature (Q) modules.

Now, with reference to FIG. 5, signal separation using spreading codes will be considered. The receiving antenna array 342 contains N antenna elements 343 for receiving at least N different sums of M source signals. Code compression unit 350 is connected to N antenna elements 343 for decoding at least N different sums of M source signals. Each of N different sums includes k codes to provide k different sums of M source signals associated with it.

A receiver unit 340 is coupled to a code compression unit 350 for receiving at least kN different sums from M source signals. Blind signal separation processor 344 is coupled to receiver unit 340 to form a mixing matrix 346 containing at least kN different sums of M source signals. The mixing matrix 346 has a rank equal to kN, where the resulting rank is at least M. Blind signal separation processor 344 extracts useful source signals from the mixing matrix 346.

Depending on the modulation of the received signals, the above signal separation can be used to increase the rank of the mixing matrix without increasing the number N of antenna elements. CDMA standard systems IS-95, CDMA2000 and WCDMA are examples of spread spectrum communications systems using spreading codes. The common point is that a unique code is processed with each signal to expand the data over a larger frequency band.

The same spreading code is processed with the received sum of signals (useful signal, unwanted signals and unknown noise sources). This recreates the useful signal back to its original frequency bandwidth, while the interference spreads over a wide frequency band.

The CDMA implementations listed above actually have many signal streams that simultaneously use the same frequency band. Each signal stream uses a code that is perfectly orthogonal to all others. If this condition is met in the decoder, it means that as a result only the signal of interest will be compressed.

There is often some correlation between CDMA signals, so that along with a useful signal, interference signals are restored. This is often due to the delay experienced by individual signals, as well as the multipath propagation of signals. Some of the unwanted signals, especially CDMA signals, will increase in value. The increase will not be as significant as for a useful signal, but it will still increase the overall noise value and therefore decrease the signal-to-noise ratio.

The equation form of the compressed signals and the signals themselves meets the criterion for processing with blind separation of signals. In fact, if one of the compression codes is separately applied for each known signal received by the receiver unit 340, separate sums are obtained that meet the requirements of the ICA model.

Therefore, there are as many row entries available for the mixing matrix as there are known codes, assuming, of course, that each of them forms a linearly independent significant quantity. Under the right conditions, this will increase the mixing matrix to a value greater than the number of codes. For example, N antenna elements and M codes can provide NM matrix rows.

For illustrative purposes, it is assumed that 3 codes are known, and the signals of 3 known codes retain their orthogonality. In the code compression unit 350, the mixing matrix A has each of the 3 upper rows and 3 lower rows due to the antenna stream, after each stream has been compressed using 3 known codes. The diagonal values are 0 due to the orthogonality of the codes. The entries in columns 4, 5 and 6 are for the general case of unknown signals of the same index.

The signals corresponding to the entries in columns 4, 5 and 6 may be other versions of the transmission path of known codes or other cellular signals of unknown codes. In addition, one signal can be Gaussian, and the other signal can be any group of CDMA signals obeying the central limit theorem, so they look like one Gaussian signal, for example, 4 channels. In other words, a sufficient number of nonrandom signals will be added to the Gaussian signal. Interference sources may be sources of non-Gaussian signals or at least one Gaussian signal unknown to the network.

After the known codes are compressed in the code compression unit 350, the blind separation processor 344 receives a rank 6 mixing matrix 346. Rank 6 is obtained based on 2 antenna elements times 3 by 3, since 3 codes are known.

6 signals are supplied to the blind signal separation processor 344, in which a mixing matrix 346 having a rank 6 is formed. The blind signal separation processor 344 determines the separation matrix W only from the received signals changed by channels: x = As, where A is the mixing matrix. In the illustrated example, 6 signals are allocated.

Blind signal separation processor 344 selects signals to be decoded. For example, signals from interference sources may be discarded, and all versions of useful signals selected. The selected signals are fed to the demodulator module for demodulation. The demodulator uses well-known equalization methods that combine multipath versions of the same signal.

In a more general case, the values on the diagonal, for simplicity shown above as 0, can actually be nonzero. A more common case will be one in which the correlation properties between the encoded signals are not perfect. This will represent additional noise for each selected signal. However, as shown earlier, the matrix rank is sufficient to isolate these signals, so their value will be significantly reduced after processing with blind separation of signals. This leads to a decrease in noise, an increase in the signal-to-noise ratio and, as indicated by Shannon's law, an increase in channel capacity.

Now turn to Fig.6. Another approach to increasing the rank of the mixing matrix without increasing the number N of antenna elements is to divide the received mixed signal into its in-phase (I) and quadrature (Q) components. Components I and Q of a coherent radio signal are components whose amplitudes are the same and the phases are 90 degrees apart.

The receiving antenna array 442 contains N antenna elements 443 for receiving at least N different sums from M source signals. The corresponding module 450 in-phase and quadrature components is located in the downward direction from each antenna element 443 to extract each of N different sums from the M source signals, thereby being received in the set of in-phase and quadrature components.

The receiver unit 440 is in the downstream direction from each common-mode and quadrature component module 450 for receiving at least N sets of common-mode and quadrature components for at least N different sums of M source signals. Blind signal separation processor 444 is in a downstream direction from receiver unit 440 to form a mixing matrix 446 containing at least 2N different sums of M source signals. Each set of in-phase and quadrature components provides 2 entries to the mixing matrix 446. The mixing matrix 446 has a rank of up to 2N, and the blind signal separation processor 444 extracts useful signals 514 from the mixing matrix 512.

By dividing the received mixed signals into components I and Q, the size of the mixing matrix is doubled. Since the I and Q components are encoded using different data streams, the mixed signal received at any antenna element can be split into two different mixed signals.

In the case of differential coding, the nature of the modulation should be analyzed to determine if components I and Q meet the linearity requirement. For example, for the Global System for Mobile Communications (GSM), it has been shown that GMSK encoding can be considered linear when used with appropriate filtering and receiver processing, as if it were binary phase shift keying (BPSK) encoding. Since the BPSK meets the requirements for signal blind processing, the described process of I and Q components can be used.

Components I and Q can be used with any of the antenna array embodiments described above to populate the mixing matrix A. When using components I and Q, the mixing matrix A can be filled, as if a 2 times larger number of antenna elements were used. Antenna elements can have any kind of diversity, for example, uncorrelated, correlated, or polarized. N antenna elements when dividing the sum of the signal of each element into components I and Q provide 2N independent mixed sums of signals. As a result, the rank of the mixing matrix is 2N, where 2N is at least equal to or greater than M.

This mechanism can also be used with the antenna deflection method to create more signal sums. Each of these sums can in turn also be divided into components I and Q. The coefficient 2 from separation into components I and Q, N antenna elements and K deviation regions for the antenna array provide 2KN sums for the mixing matrix.

Many modifications and other embodiments of the invention will be understood by those skilled in the art based on the solutions presented in the foregoing description and the related drawings. Therefore, it is understood that the invention should not be limited to the particular embodiments disclosed, and that modifications and embodiments are included within the scope of the appended claims.

Claims (24)

1. Communication system with multiple inputs and multiple outputs (MIMO), containing:
a transmitter configured to transmit M source signals;
a transmitting antenna array connected to said transmitter and comprising M antenna elements for transmitting M source signals;
a receiving antenna array containing N antenna elements for receiving at least M different sums from M source signals, where N is less than M;
a receiver connected to the specified receiving antenna array and made
with the possibility of receiving at least M different amounts from M source signals, and
a signal separation processor coupled to said receiver for generating a mixing matrix comprising at least M different sums of M source signals, so that the mixing matrix has a rank of at least M;
moreover, said signal separation processor is designed to extract useful source signals from a mixing matrix. 2. The MIMO communication system according to claim 1, in which L interference source signals exist and interfere with extracting useful source signals from the mixing matrix, where L is greater than 1; wherein said mixing matrix also increases its rank with L sums in addition to M different sums from M source signals so that the mixing matrix has a rank of at least M + L. 3. The MIMO communication system according to claim 1, wherein said receiving antenna array contains N correlated antenna elements for forming a phased antenna array. 4. The MIMO communication system according to claim 1, wherein said receiving antenna array contains N correlated antenna elements, said N correlated antenna elements contain at least one active antenna element and up to N-1 passive antenna elements for forming a switched antenna ray. 5. The MIMO communication system according to claim 1, in which each sum of M source signals is linear. 6. The MIMO communication system according to claim 1, wherein said receiving antenna array contains N correlated antenna elements, and at least two of said N correlated antenna elements have different polarizations. 7. The MIMO communication system according to claim 6, in which the various polarizations are orthogonal to each other. 8. The MIMO communication system according to claim 1, wherein said receiving antenna array generates N initial antenna patterns for receiving at least N different sums from M source signals; and further comprises a tilt controller connected to said receiving antenna array for selectively varying the tilt of at least one of the N initial antenna patterns so that at least one additional other antenna pattern for receiving is formed at least one additional sum of M source signals; moreover, the mixing matrix additionally contains at least one additional other sum of M source signals, the mixing matrix has a rank equal to N plus the number of different different sums of M source signals received using additional radiation patterns of the antenna, the resulting rank is at least M. 9. The MIMO communication system according to claim 1, wherein said receiving antenna array generates at least N antenna beams for receiving at least N different sums of M source signals, where N and M are greater than 2; and further comprises a controller connected to said receiving antenna array for selectively generating at least N antenna beams; wherein said signal separation processor also determines whether different sums of M source signals are correlated or statistically independent, and if not, then interacts with said controller to generate different beams to receive different new sums of M source signals to replace different sums of M source signals that are not correlated or statistically independent in the mixing matrix, so that the rank is equal to at least M. 10. The MIMO communication system according to claim 1, further comprising a code compression unit coupled to said N antenna elements for decoding at least N different sums of M source signals, each of N different sums containing k codes to provide k different sums ; wherein said receiver is connected to said code compression unit for receiving at least kN different sums from M source signals; wherein said signal separation processor generates a mixing matrix comprising at least kN different sums of M source signals, with the resulting rank equal to at least M. 11. The MIMO communication system according to claim 1, additionally containing a module of in-phase and quadrature components, connected in a downward direction with each antenna element in said receiving antenna array for dividing each of the received M different sums from M source signals into a set of in-phase and quadrature component ; wherein said signal separation processor forms a mixing matrix containing at least 2N different sums of M source signals, each set of in-phase and quadrature components provides 2 input entries to the mixing matrix with the resulting rank equal to at least 2N, where 2N is at least M. 12. The MIMO communication system according to claim 1, wherein said signal separation processor comprises a blind signal separation processor and extracts useful source signals from the mixing matrix based on at least one of the main components analysis (PCA), analysis of the independent components (ICA ) and singular decomposition (SVD). 13. The MIMO communication system according to claim 1, wherein said signal separation processor extracts useful source signals from the mixing matrix based on a knowledge-based signal extraction process, and the knowledge-based processing of signal extraction extracts useful source signals from the mixing matrix based on at least one of the process of vanishing insignificant coefficients (ZF) and the process of minimum mean square estimation (MMSE). 14. The MIMO communication system of claim 1, wherein said signal separation processor extracts useful source signals from a mixing matrix based on a combination of the knowledge-based signal extraction process and blind signal separation process. 15. A method for controlling a communication system with multiple inputs and multiple outputs (MIMO communication system), comprising the steps of:
controlling a transmitter configured to transmit M source signals;
transmitting M source signals from a transmitting antenna array connected to the transmitter, the transmitting antenna array containing M antenna elements;
receive at least M different sums of M source signals from the receiving antenna array, wherein the receiving antenna array contains N antenna elements, where N is less than M;
outputting to the receiver at least M different amounts from M source signals; and process, by means of a blind signal separation processor coupled to the receiver, at least M different sums from M source signals, said processing comprising the steps of: generating a mixing matrix comprising at least M different sums from M source signals so that the mixing matrix has a rank of at least M, and useful source signals are extracted from the mixing matrix. 16. The method of claim 15, wherein L interference source signals exist and interfere with extracting useful source signals from the mixing matrix, where L is greater than 1; further comprising adding L additional sums to at least M different sums of the M source signals so that the mixing matrix has a rank of at least M + L. 17. The method according to clause 15, in which the receiving antenna array contains N correlated antenna elements for forming a phased antenna array. 18. The method according to clause 15, in which the receiving antenna array contains N correlated antenna elements, N correlated antenna elements contain at least one active antenna element and up to N-1 passive antenna elements for forming a switched beam antenna. 19. The method according to clause 15, in which the receiving antenna array contains N correlated antenna elements, and at least two of the N correlated antenna elements have different polarizations. 20. The method according to clause 15, in which the receiving antenna array generates N initial antenna patterns for receiving at least N different sums of M source signals; and further comprises a tilt controller connected to the receiving antenna array, for selectively changing the tilt of at least one of the N initial antenna patterns so that at least one additional other antenna pattern is formed for receiving at least at least one additional sum of M source signals; moreover, the mixing matrix additionally contains at least one additional other sum of M source signals, and the mixing matrix has a rank equal to N plus the number of additional different sums of M source signals received using additional radiation patterns of the antenna, and obtained in the result is a rank of at least M. 21. The method according to clause 15, in which the receiving antenna array generates at least N antenna beams for receiving at least N different sums of M source signals, where N and M are greater than 2; and further comprises a controller connected to the receiving antenna array for selectively generating at least N antenna beams, said blind signal separation processor also determining whether various sums of M source signals are correlated or statistically independent, and if not, then interacts with the controller to form different beams for receiving different new sums from M source signals to replace different sums from M source signals that are not correlated solid or statistically independent in the mixing matrix so that the rank is at least M. 22. The method of claim 15, further comprising a code compression unit coupled to N antenna elements for decoding at least N different sums of M source signals, each of N different sums containing k codes to provide k different sums; moreover, the receiver is connected to a code compression unit for receiving at least kN different sums from M source signals; moreover, the processor blind separation of the signals forms a mixing matrix containing at least kN different sums of M source signals, with the resulting rank equal to at least M. 23. The method according to clause 15, further comprising a module of the in-phase and quadrature components, connected in a downward direction with each antenna element in the receiving antenna array to separate each of the received M different sums from the M source signals into a set of in-phase and quadrature component; moreover, the processor for blind separation of signals forms a mixing matrix containing at least 2N different sums of M source signals, each set of in-phase and quadrature components provides 2 input entries to the mixing matrix with the resulting rank equal to at least 2N where 2N is at least M. 24. The method according to clause 15, in which the selection of useful source signals from the mixing matrix is based on at least one of the knowledge-based process of extracting signals and the process of blindly extracting signals, the process of extracting signals with knowledge-based processing extracts useful signals source from the mixing matrix based on at least one of the process of vanishing of insignificant coefficients (ZF) and the process of minimum root mean square estimation (MMSE).

Images