CN101867402B - MIMO system and application method thereof for adaptive antenna selection - Google Patents

Abstract

The invention discloses an MIMO system and an application method thereof for adaptive antenna selection. The MIMO system comprises a transmitter, a receiver and a wireless channel; the process of the antenna selection which is realized in the MIMO system adopts an iterative feedback structure; a subset is selected quickly from a large amount of antenna sets by using the characteristic of 60 GHz channel without accurate channel estimation, so that a subchannel corresponding to an antenna subarray is optimal in the sense of a defined target function; and high speed data transmission is performed by using the selected optimal antenna subarray. The invention provides the rapid and efficient application method for the MIMO system along with low cost, and solves the technical problems of high complexity and low efficiency of the traditional antenna selection method in the large-scale the MIMO system. An experiment proves that: the adopted method can obtain good performance only through several iterations and low computational complexity; and the system can better support the application of the large-scale the antenna array.

Description

Mimo system and application process thereof that a kind of adaptive antenna is selected

Technical field:

The invention belongs to the communications field, relate to a kind of mimo system and application process thereof, especially a kind of adaptive antenna system of selection and signal processing method thereof based on random optimization.

Background technology:

The transmitting terminal of multiple-input and multiple-output (MIMO) system or receiving terminal or both have a plurality of antennas conventionally to form aerial array.Utilize spatial reuse or transmit diversity, mimo system can obtain the gain of channel capacity, improves the transmission performance of system.Therefore, mimo system (or multiaerial system) becomes the first-selected physical layer architecture of following high-speed communication system.60GHz communication system, this is considered to provide the system up to several Gbps transmission rates to adopt multi-antenna array as receiving and transmitting front end not at all surprising.Be devoted in a motion of standardized 802.15 working groups of 60GHz also will application fairly large aerial array (dual-mode antenna array respectively has 32 element antennas) and wave beam forming (beamforming) as the key technology of physical layer.

Although large-scale aerial array is being employed progressively, the needed independent radio frequency link number of communication system transmit-receive end is but because the restriction of the factors such as cost and power consumption can not increase arbitrarily thereupon.Sub-array antenna selects technology to select the sub-array antenna of one group of optimum from all antenna combinations, and it is connected with existing radio frequency link.Because this technology takes full advantage of larger aerial array with fewer object independence radio frequency link, with the power consumption compared with low and cost, can select to close some radio frequency link or only use fewer object antenna to receive and dispatch and can obtain full array gain and receive much concern, thereby becoming and reduce the key technology that the multiaerial system of system complexity and power consumption is optimized.Undoubtedly, this meaning in the extensive antenna array system of 60GHz is more outstanding.In existing mimo system, the Antenna Selection Algorithem realizing all supposes that the wireless channel between receiver/transmitter is known or can obtains by channel estimating, and by target function corresponding to optimization sub-array antenna (capacity or performance of BER), finally obtains optimum sub-array antenna on this basis.Yet, in the 60GHz communication system of the extensive aerial array of application, consider that channel matrix is unknown, and dimension being larger, the estimated value that obtains accurately huge channel matrix is difficult.In addition, more existing iterative algorithms hour can be used in number of antennas, but when number of antennas further increases, because search volume is huge, the algorithmic statement performance realizing is very poor, the efficiency of it line options is comparatively low, makes the efficiency of mimo system aerial array adaptive optimization lower.

Summary of the invention:

For in view of above-mentioned existing sub-array antenna system of selection mimo system is at 60GHz, at a high speed, deficiency under extensive many antenna applications background in system, particularly the optimized algorithm of sub-array antenna relies on accurate channel estimation results otherwise is difficult to obtain good performance, the present invention is intended to disclose mimo system and the application process thereof that a kind of adaptive antenna is selected, this mimo system adopt the structure of iterative feedback, utilize the feature of 60GHz channel, the scalar output that comprises noise according to receiving terminal rather than under the condition without accurate channel estimating, utilize the feature of 60GHz channel, from the larger antenna set of number, choose fast an optimum antenna subset, after being connected with radio frequency link, it adopt the method for beam forming to carry out transfer of data at a high speed, make the subchannel that selected sub-array antenna is corresponding is optimum under defined target function meaning.

The technical problem to be solved in the present invention is to provide extensive MIMO antenna array system under a kind of 60GHz of being operated in channel condition and quick self-adapted optimization method and the signal processing method of sub-array antenna thereof, solve existing mimo system antenna under above-mentioned application background and optimize the lower problem of efficiency of selection, thereby effectively reduce power consumption and the cost of mimo system, improve quick day line options problem of the robustness of system.The present invention includes transmitter, receiver and wireless channel model.

Described transmitter (flowing to by signal) comprises information source module, submodule is made a start in the baseband processing module of making a start (comprise coding, interweave, scrambling, modulation etc.), the beam forming device (multiplier) of making a start, a plurality of parallel radio frequency link of making a start (comprising upconverter, low noise amplifier, linear filter etc.), make a start aerial array and adapter thereof, sub-array antenna selection.The described sub-array antenna submodule of making a start comprises antenna index manager and on-off controller.

Described receiver (flowing to by signal) comprises receiving antenna array and adapter, receiving end radio frequency link (comprising frequency converter, filter etc.), receiving end beam forming device (multiplier), receiving end baseband processing module (comprising demodulation, descrambling, deinterleaving, decoding etc.), receiving end mixer (adder), the stay of two nights and sub-array antenna are selected receiving end submodule.Described sub-array antenna receiving end submodule comprises antenna index manager and on-off controller module, target function estimation logic and iteration are upgraded control module etc.

Described wireless channel is 60GHz wireless channel, it is characterized by (the line of sight that generally there is stronger sighting distance, LOS) component, Mathematical Modeling represents with the random matrix H that ranks number equals dual-mode antenna conventionally, its element is obeys the stochastic variable that Rice distributes.In addition, between receiver/transmitter, there is a low rate, reliable feedback link, for instructing the renewal selection result of making a start.

Described (receipts) end beam forming device utilizes multiple antenna that signal is projected in the subspace consisting of beam forming weights and transmitted, and has reduced the interference between multiple signals, has obtained array (beam forming) gain.

At described (receipts) letter machine place, at least there is a radio frequency link module (comprising frequency mixer, low noise amplifier, filter etc.).

The signal that described receiving end mixer receives each reception antenna is exported after according to the weighted sum of receiving end beam forming weights.

Described mimo system has N _tindividual transmitting antenna and N _rindividual reception antenna, here N _tand N _rall larger.In transmitter and receiver, there is respectively n _tand n _rindividual available radio frequency link, and n _t≤ N _t, n _r≤ N _r.

Described mimo system replaces is operated in two different processes, and process one, and dual-mode antenna subarray selection course, has N _tindividual transmitting antenna and N _rindividual reception antenna, here N _tand N _rall larger, as 32.In addition, in transmitter and receiver, there is respectively n _tand n _rindividual available radio frequency link (n _t≤ N _t, n _r≤ N _r).The function that described sub-array antenna chooser module completes be from

in individual possible antenna combination, select a kind of combination, make the corresponding wireless subchannel of this sub-array antenna is optimum under the meaning of defined target function.Process two, data transmission procedure, data message is processed by beam forming device by baseband processing module, then launches and be received machine reception through selected bay in radio frequency link and process one, completes a data transfer.

In order to solve the problems of the technologies described above, the signal processing method that the present invention also provides the adaptive antenna subarray in described mimo system to select, said method comprising the steps of:

(1) system enters sub-array antenna selection course (being process one), one group of unduplicated antenna index sequence subset ω of the random generation of antenna index manager ⁽⁰⁾; Using this antenna subset as initial current sub-array antenna ω=ω ⁽⁰⁾selected sub-array antenna and the vector of the probability Estimation in initialization iteration update controller

(2) sampling, estimation and iterative process: each iterative process is decomposed into n _t+ n _rindividual sub-iterative process; In k sub-iteration, antenna index manager substitutes k element in current antenna subset by a new random antenna index, and obtaining one only has k the new antenna subset that element is different from current antenna subset

transmitting training sequence (as complete 1 sequence) is also estimated two antenna subset ω in target function estimation logic module ⁽ⁿ⁾with

corresponding target function φ (ω ⁽ⁿ⁾) and

(3) adaptive-filtering process.First compare φ (ω ⁽ⁿ⁾⁾with

size, and the greater is decided to be to the current sub-array antenna of next iteration,

${\mathrm{\ω}}^{(n+1)}=\left\{\begin{array}{cc}{\mathrm{\ω}}^{\left(n\right)},& \mathrm{\φ}\left({\widehat{\mathrm{\ω}}}^{\left(n\right)}\right)>\mathrm{\φ}\left({\widetilde{\mathrm{\ω}}}_k^{\left(n\right)}\right)\\ {\widetilde{\mathrm{\ω}}}_k^{\left(n\right)},& \mathrm{\φ}\left({\widehat{\mathrm{\ω}}}^{\left(n\right)}\right)\≤\mathrm{\φ}\left({\widetilde{\mathrm{\ω}}}_k^{\left(n\right)}\right)\end{array}\right.,$

Secondly, check that whether it has record in probability Estimation vector π, if do not recorded, needs to record π=[π (ω for it appends one ⁽ⁿ⁺¹⁾, 0) ^t]; Then the record in current probability Estimation vector being done to adaptive-filtering processes:

π ⁽ⁿ⁺¹⁾=[1-μ(n+1)]π ⁽ⁿ⁾

π ⁽ⁿ⁺¹⁾(ω)=π ⁽ⁿ⁺¹⁾(ω)+μ(n+1)

In formula, π ⁽ⁿ⁺¹⁾represent π ⁽ⁿ⁾probability vector after upgrading once; π ⁽ⁿ⁾(ω) represent the record of antenna subset ω in probability vector; μ (n)=1/n is the forgetting factor of adaptive process, increases and diminishes, the intensity that reflection is once upgraded with iterations;

(4) select and iteration renewal process: last in each iterative process, according to the probability Estimation vector after upgrading, the antenna subset of therefrom selecting a probability Estimation value maximum selects antenna subset as this iteration, this result will notify the antenna index manager of making a start to upgrade the current antenna index information being connected with radio frequency link by feedback link by iteration update controller.In the same way, receiving end antenna index manager obtains this information and executing index upgrade by inner passage.

(5) sub-iteration continues, until k>n _t+ n _r;

(6) if the condition of convergence meets, iterative process finishes, otherwise will proceed to next iterative process.

Suppose that channel matrix is H, the optimization aim function definition of certain antenna subset is H ^hthe dominant eigenvalue of H, i.e. λ ₁(H ^hh), it is equivalent to the main singular value of channel matrix, () ^hfor conjugate transpose.Described step (2) refers to:

(a) be antenna subset ω estimation target function: information source input training sequence is also launched by the bay in antenna subset ω, through corresponding wireless channel H _ω, after merging by the weighting of receiving end beam forming weight coefficient, can obtain the output signal corresponding to k transmitting antenna h wherein _kbe that k transmitting antenna is to the wireless channel of receiving antenna array; V (k) is additive white Gaussian noise;

(b) in order to eliminate the impact of noise, independently repeat step a) M time, obtain y ^(m)(k), 1≤k≤n _t, 1≤m≤M; And further do following smoothing processing

$\mathrm{\β}\left(k\right)=\frac{1}{M}\left\{\right[y^{\left(1\right)}{\left(k\right)}^H{y}^{\left(2\right)}\left(k\right)+y^{\left(2\right)}{\left(k\right)}^H{y}^{\left(3\right)}\left(k\right)+\·\·\·+y^{\left(M\right)}{\left(k\right)}^H{y}^{\left(1\right)}\left(k\right)]$

$+\underset{l=1,l\≠k}{\overset{n_t}{\mathrm{\Σ}}}\left|\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{y}^{\left(m\right)}{\left(k\right)}^H{y}^{\left(m\right)}\left(l\right)\right|\}$

By following formula, obtain the target function estimated value of antenna subset ω

$B=\frac{1}{\sqrt{n_t}}\max \{\mathrm{\β}\left(1\right),\mathrm{\β}\left(2\right),\·\·\·,\mathrm{\β}\left(n_t\right)\}\≈{\mathrm{\λ}}_1\left(H_{\mathrm{\ω}}^H{H}_{\mathrm{\ω}}\right)$

The realization of target function estimation logic as above, without accurate channel estimating, has reduced to a great extent the complexity of system and has realized cost.

The antenna selection course of described system obtains the sub-array antenna of one group of optimum while finishing, it is connected with radio frequency link and forms the mimo system of an optimization.Under the hypothesis of quasi-static channel opens, system can enter process two, utilizes beam forming technology technology to carry out high speed data transfer.If channel changes, system can again enter process two and select optimum sub-array antenna for transfer of data.

Accompanying drawing explanation:

Fig. 1 is the mimo system structure chart based on beam forming method of the present invention;

Fig. 2 is the flow chart that the present invention is based on sequential stochastic approximation optimized algorithm;

Fig. 3 is the performed program flow diagram of target function estimation logic of the present invention;

Fig. 4 is average behavior curve chart of the present invention.

Wherein: be respectively 1 for information source; 2 is transmitting terminal Base-Band Processing; 3 is transmitting terminal multiplier; 4 is transmitting terminal beam forming device; 5 is transmitting terminal parallel radio frequency link; 6 is transmitting terminal radio frequency link and antenna a period of time adapter; 7 is transmitting terminal aerial array; 8 is transmit antenna selection module; 8.1 is transmitting terminal antenna index manager; 8.2 is transmitting terminal on-off controller; 9 is receiving terminal Antenna Selection Module; 9.1 is receiving terminal antenna index manager; 9.2 is that receiving terminal on-off controller 9.3 is target function estimation logic; 9.4 is iteration control device; 10 is receiving terminal aerial array; 11 is receiving terminal radio frequency link and aerial array adapter; 12 is receiving terminal parallel radio frequency link; 13 is receiving terminal beam forming device; 14 is receiving terminal multiplier; 15 is adder; 16 is receiving terminal baseband processing module; 17 is the stay of two nights.

Embodiment:

Below in conjunction with accompanying drawing, the present invention is described in further detail:

Referring to Fig. 1,2,3,4, MIMO Antenna Selection Module disclosed in this invention adopts the stochastic approximation optimized algorithm of sequential scheme to optimize fast and select and then effectively reduce complexity and the cost that system realizes the sending and receiving end sub-array antenna of the extensive antenna array system of 60GHz.In order to maximize the beam forming gain of mimo system, in described method, related optimization aim function definition is the main singular value of subchannel matrix that aerial array is corresponding.The target function of related day line options is not to be calculated and obtained by the result of channel estimating, but utilize 60GHz channel to there is stronger this feature of sighting distance component, by the scalar output signal receiving, estimate that the lower bound of your the disk right margin of lid of channel matrix effectively obtains the estimation of target function.

Below in conjunction with diagram, to adaptive MIMO sub-array antenna of the present invention, select the implementation method of module to be elaborated.

Fig. 1 is mimo system and the adaptively selected structural representation of sub-array antenna based on beam forming technology of the present invention.

Take alone family as example, and transmitting-receiving two-end has respectively N _r=10 and N _tthe aerial array of=32 array elements.N _rand n _trespectively the radio frequency link number that transmitting-receiving two-end can be used, and n _t≤ N _t, n _r≤ N _r.Sub-array antenna selects module will select n from 32 transmitting antennas exactly _tindividual and select n from 10 reception antennas _rindividual composition sub-array antenna is for transfer of data, this is combined in and in all combinations, has optimum target function value.For the convenience of explaining, the adaptively selected of transmitting antenna of only take is example explanation, i.e. n _t=10; n _r=10=N _r.Should be understood that, if only consider reception antenna or consider that the adaptively selected of dual-mode antenna is naturally extending of described method simultaneously, also within the protection range of this invention.

On the whole, be positioned at the antenna chooser module of transmitter and receiver according to the instruction of selection algorithm, by antenna index manager and on-off controller, the training sequence of information source input being connected to selected transmitting antenna array element launches, and by estimating and judge the rear iteration renewal control information of exporting by again passing into antenna chooser module after output invariant signal after reception antenna reception merging after wireless channel, next according to this information, the antenna index manager of transmitting-receiving two-end is made accordingly more new element.Until sub-array antenna selection course finishes (program circuit is as shown in Fig. 2 Fig. 3).Specifically, as described below:

A) system enters sub-array antenna selection course, and it is unit matrix that the beam forming weight coefficient of making a start is set

(that is, except diagonal entry non-zero, other elements are zero), arranges receiving end beam forming weight coefficient and is

(wherein

square formation for element complete 1); One group of unduplicated antenna index sequence subset of the random generation of antenna index manager,

a _i∈ 1,2 ..., N _tand a _i≠ a _j, i ≠ j.Using this antenna subset as the initial sub-array antenna that selects

and the vector of the probability Estimation in initialization iteration update controller π=(ω ⁽⁰⁾, 1) ^t.

B) enter iterative process.Each iterative process is decomposed into n _tindividual sub-iterative process.In k sub-iteration, by current, selected k element in antenna subset to substitute by a new random antenna index, obtaining one only has k the element new antenna subset different from selecting antenna subset

launch complete 1 training sequence and utilize following steps to calculate each antenna subset

with

corresponding target function:

C) general, suppose to be antenna subset ω estimation target function this moment.Information source is inputted complete 1 training sequence signal and is launched by the bay in antenna subset ω, through wireless channel H _ω, after merging by the weighting of receiving end beam forming weight coefficient, can obtain the output signal corresponding to k transmitting antenna

h wherein _kbe that k transmitting antenna is to the wireless channel of receiving antenna array; V (k) is additive white Gaussian noise.

D) in order to eliminate the impact of noise, independently repeat step a) M time, obtain y ^(m)(k), 1≤k≤n _t, 1≤m≤M.And further, obtain

$\mathrm{\β}\left(k\right)=\frac{1}{M}\left\{\right[y^{\left(1\right)}{\left(k\right)}^H{y}^{\left(2\right)}\left(k\right)+y^{\left(2\right)}{\left(k\right)}^H{y}^{\left(3\right)}\left(k\right)+\·\·\·+y^{\left(M\right)}{\left(k\right)}^H{y}^{\left(1\right)}\left(k\right)]$

$+\underset{l=1,l\≠k}{\overset{n_t}{\mathrm{\Σ}}}\left|\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{y}^{\left(m\right)}{\left(k\right)}^H{y}^{\left(m\right)}\left(l\right)\right|\}$

Estimator with target function

$B=\frac{1}{\sqrt{n_t}}\max \{\mathrm{\β}\left(1\right),\mathrm{\β}\left(2\right),\·\·\·,\mathrm{\β}\left(n_t\right)\}$

Can prove, in the direct-view path of channel, estimator B is the estimator of the corresponding target function of sub-array antenna during strong and M → ∞.It should be noted that owing to estimating that in every sub-iteration two antenna subsets of target function only have an antenna element different, therefore, in estimation process, only need launch one time training sequence on same antenna element.

1) in every sub-iteration of step 2, obtained the target function of two antenna subsets with

then to do the work of two aspects.Check that on the one hand whether that antenna subset that target function is larger has record in probability Estimation vector, if do not recorded, needs to append a record for it; On the other hand, every record in current probability Estimation vector is done to adaptive-filtering operation:

π ⁽ⁿ⁺¹⁾=[1-μ(n+1)]π ⁽ⁿ⁾

π ⁽ⁿ⁺¹⁾(ω)=π ⁽ⁿ⁺¹⁾(ω)+μ(n+1)

In formula, π ⁽ⁿ⁾the probability vector that represents the n time iteration; π ⁽ⁿ⁾(ω) represent that antenna subset ω is in probability vector

Record; μ (n)=1/n is the forgetting factor of adaptive process, represents the intensity of once upgrading.

Select and iteration renewal process.Last in each iterative process, according to upgrading probability Estimation vector later, therefrom selects the antenna subset of a probability Estimation value maximum as the antenna selection result of this iteration.If the condition of convergence meets, finish, otherwise will proceed to next iterative process.

Simulation result:

In the realization of above-mentioned example, use following parameter setting:

Number of transmit antennas: N _t=32; Transmitting terminal radio frequency link number: n _t=10

Reception antenna number: N _r=10; Receiving terminal radio frequency link number: n _r=10

The wireless channel model Rician K factor: K=10dB;

Receiving terminal average signal-to-noise ratio: 10dB

Target function is estimated level and smooth number of times: M=10

According to said system parameter, carry out 100 independently Computer Simulations, and obtain the average behavior curve of system works as shown in Figure 4.For relatively, also provided the performance of traditional iterative algorithm.Because the number of antennas of instance system is more, cannot use the method for exhaustion to obtain theoretical performance, so in all search volumes, random 1000 sky line options of selection, and find out and wherein have result best and the poorest performance as theoretical performance, and compare with proposed method.

Can find out, the estimation of sequential Stochastic Optimization Algorithms of the present invention next time of right margin of your disk of lid, its value is slightly larger than real target function, but overall trend is consistent.Can prove, along with the increase of M and K, this gap will be dwindled.The iterative algorithm that the algorithmic statement performance that proposes is more traditional is good, just due to the latter's efficiency of algorithm high causing not huge in search volume in the situation that, and SEQUENTIAL ALGORITHM of the present invention by each random search be limited to last " near ", be conducive to the maintenance of good results.In addition, the method applied in the present invention only needs the iteration of minority, and performance has been better than in 1000 random selections best, and this has also embodied the method adopting and has had higher efficiency and asymptotic optimization.

Above content is in conjunction with concrete preferred implementation further description made for the present invention; can not assert that the specific embodiment of the present invention only limits to this; for general technical staff of the technical field of the invention; without departing from the inventive concept of the premise; can also make some simple deduction or replace, all should be considered as belonging to the present invention and determine scope of patent protection by submitted to claims.

Claims (3)

1. a mimo system of selecting based on adaptive antenna, is characterized in that: comprise transmitter, receiver and wireless channel, wherein, be provided with for instructing to make a start and upgrade the feedback link of selection result between described transmitter and receiver, described transmitter comprises information source module, the baseband processing module of making a start, the beam forming device of making a start, a plurality of parallel radio frequency links of making a start, the aerial array of making a start, the aerial array adapter of making a start, sub-array antenna selects to make a start submodule, described information source module is connected with the baseband processing module of making a start, the baseband processing module output of making a start is connected with a plurality of beam forming devices of making a start, each beam forming device of making a start is all connected with the radio frequency link of making a start, the radio frequency link of making a start is connected with the aerial array adapter of making a start, the aerial array adapter of making a start is connected with the aerial array of making a start, the sub-array antenna submodule input of selecting to make a start is connected with feedback link, the sub-array antenna submodule output of selecting to make a start is connected with the aerial array adapter of making a start, described receiver comprises receiving antenna array, receiving antenna array adapter, receiving end radio frequency link, receiving end beam forming device, receiving end baseband processing module, receiving end mixer, the stay of two nights, sub-array antenna is selected receiving end submodule, receiving aerial array is connected with receiving antenna array adapter, receiving antenna array adapter connects output and is connected with receiving end radio frequency link, receiving end radio frequency link is connected with receiving end beam forming device, receiving end beam forming device connects and is connected with receiving end mixer, receiving end mixer is connected with receiving end baseband processing module, receiving end baseband processing module output is connected with the stay of two nights, sub-array antenna selects receiving end submodule input to be connected with receiving end mixer output, sub-array antenna selects receiving end submodule output to be connected with feedback link and receiving antenna array adapter respectively, the described sub-array antenna submodule of making a start comprises successively antenna index manager and the on-off controller connecting, described sub-array antenna receiving end submodule comprises target function estimation logic module, iteration update controller, antenna index manager and the on-off controller module connecting successively, one group of unduplicated antenna index sequence subset of the random generation of described antenna index manager, in target function estimation logic module, estimate two antenna subsets, iteration update controller notifies the antenna index manager of making a start to upgrade the current antenna index information being connected with radio frequency link by feedback link, described wireless channel is 60GHz wireless channel.

2. a kind of application process of the mimo system of selecting based on adaptive antenna as claimed in claim 1, is characterized in that:

(1) described system alternation is at sub-array antenna selection course and data transmission procedure; Described sub-array antenna selection course refers to that sub-array antenna select to make a start submodule and sub-array antenna select receiving end submodule all in aerial array, to select an optimum subarray for transfer of data; Described data transmission procedure refers to and utilizes selected optimal antenna subarray to carry out high speed data transfer in conjunction with beam forming technology;

(2) system enters sub-array antenna selection course, and antenna index manager and the antenna index manager in sub-array antenna receiving end submodule that sub-array antenna is made a start in submodule generate one group of unduplicated antenna index sequence subset ω at random ⁽⁰⁾; Using this antenna subset as initial current sub-array antenna ω=ω ⁽⁰⁾selected sub-array antenna

and the vector of the probability Estimation in initialization iteration update controller

(3) sampling, estimation and iterative process: each iterative process is decomposed into n _t+ n _rindividual sub-iterative process; In k sub-iteration, antenna index manager and the antenna index manager in sub-array antenna receiving end submodule that sub-array antenna is made a start in submodule substitute k element in current antenna subset by a new random antenna index, obtaining one only has k the element new antenna subset different from selecting antenna subset transmitting training sequence is also estimated two antenna subsets in target function estimation logic module

with corresponding target function

with

wherein, n _rand n _tit is respectively the radio frequency link number that transmitting-receiving two-end can be used;

(4) adaptive-filtering process: first compare

with

size, and the greater is decided to be to the current sub-array antenna of next iteration,

${\mathrm{\ω}}^{(n+1)}=\left\{\begin{array}{c}{\mathrm{\ω}}^{\left(n\right)},\mathrm{\φ}\left({\widehat{\mathrm{\ω}}}^{\left(n\right)}\right)>\mathrm{\φ}\left({\widetilde{\mathrm{\ω}}}_k^{\left(n\right)}\right)\\ {\widetilde{\mathrm{\ω}}}_k^{\left(n\right)},\mathrm{\φ}\left({\widehat{\mathrm{\ω}}}^{\left(n\right)}\right)\≤\mathrm{\φ}\left({\widetilde{\mathrm{\ω}}}_k^{\left(n\right)}\right)\end{array}\right.$

Secondly, check that whether it has record in probability Estimation vector π, if do not recorded, needs to record π=[π (ω for it appends one ⁽ⁿ⁺¹⁾, 0) ^t]; Then the record in current probability Estimation vector being done to adaptive-filtering processes:

π ⁽ⁿ⁺¹⁾=[1-μ(n+1)]π ⁽ⁿ⁾

π ⁽ⁿ⁺¹⁾(ω)=π ⁽ⁿ⁺¹⁾(ω)+μ(n+1)

In formula, π ⁽ⁿ⁺¹⁾represent π ⁽ⁿ⁾probability vector after upgrading once; π ⁽ⁿ⁾(ω) represent the record of antenna subset ω in probability vector; μ (n)=1/n is the forgetting factor of adaptive process, the intensity that reflection is once upgraded;

(5) select and iteration renewal process: last in each iterative process, according to the probability Estimation vector after upgrading, the antenna subset of therefrom selecting a probability Estimation value maximum selects antenna subset as this iteration,

this result will notify the antenna index manager of making a start to upgrade the current antenna index information being connected with radio frequency link by feedback link by iteration update controller; In the same way, receiving end antenna index manager obtains this information and executing index upgrade by inner passage;

(6) iteron iterative process, and make k=k+1, until k>n _t+ n _rfinish;

(7) return to step (3), iterative process is continued until the condition of convergence meets or surpasses maximum iteration time; If Antenna Selection Algorithem convergence enters data transmission procedure.

3. a kind of application process of the mimo system based on beam forming method as claimed in claim 2, is characterized in that, described step (3) refers to:

A) be antenna subset ω estimation target function: information source is inputted complete 1 training sequence and launched by the bay in antenna subset ω, through corresponding wireless channel H _ω, after merging by the weighting of receiving end beam forming weight coefficient, can obtain the output signal corresponding to k transmitting antenna

h wherein _kbe that k transmitting antenna is to the wireless channel of receiving antenna array; V (k) is additive white Gaussian noise;

B) in order to eliminate the impact of noise, independently repeat step a) M time, obtain y ^(m)(k), 1≤k≤n _t, 1≤m≤M; And further do following smoothing processing

$\mathrm{\β}\left(k\right)=\frac{1}{m}\left\{\right[y^{\left(1\right)}{\left(k\right)}^H{y}^{\left(2\right)}\left(k\right)+y^{\left(2\right)}{\left(k\right)}^H{y}^{\left(3\right)}\left(k\right)+\·\·\·+y^{\left(M\right)}{\left(k\right)}^H{y}^{\left(1\right)}\left(k\right)]$

$+\underset{l=1,l=k}{\overset{n_t}{\mathrm{\Σ}}}\left|\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{y}^{\left(m\right)}{\left(k\right)}^H{y}^{\left(m\right)}\left(l\right)\right|\}$

By following formula, obtain the target function estimated value of antenna subset ω

$B=\frac{1}{\sqrt{n_t}}\max \{\mathrm{\β}\left(1\right),\mathrm{\β}\left(2\right),\·\·\·,\mathrm{\β}\left(n_t\right)\}\≈{\mathrm{\λ}}_1\left(H_{\mathrm{\ω}}^H{H}_{\mathrm{\ω}}\right).$

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