CN107566017A - Method for precoding for millimeter wave mimo system - Google Patents
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Abstract
The invention belongs to wireless communication technology field, is related to a kind of method for precoding for millimeter wave mimo system.Present invention consideration is under the constraint of base station total transmission power, the maximized problem of power system capacity.The invention discloses a kind of thought using convex optimization, and BRB searching algorithms are applied into non-ideal hardware scene, pre-coding matrix optimized under conditions of total transmission power is restricted in base station, makes every effort to improve power system capacity.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a precoding method for a millimeter wave MIMO system.
Background
With the increasing tension of communication resources, in recent years, people put much attention on the research of millimeter-wave band communication, and in fact, the research on millimeter waves has started from the beginning of wireless, and has been in the past for hundreds of years, early millimeter-wave band experiments were performed by Bose, lebedev and the like as early as 1890, but millimeter-wave band communication is a relatively new field in communication. Compared with the wireless systems used commercially at present, the millimeter wave can provide higher bandwidth, and the application is very wide, for example, an open-band wireless local area network and a personal network, a 5G cellular system, a vehicle-mounted network, a networking and wearable device, and the like. MIMO signal processing techniques are also highly desirable due to the large antenna arrays used, coupled with the constraints on radio frequency and mixed signal energy. And because the bandwidth of millimeter waves is very wide, a low-complexity transceiving algorithm is very important, and compressed sensing is also very opportunistic for channel estimation and beamforming research.
Although a lot of millimeter wave precoding researches exist at present, most of the researches are concentrated on ideal hardware conditions, which is not consistent with practical application, because large-scale MIMO often generates a lot of hardware constraints due to too large antenna arrays and other reasons, it can be seen that non-ideal hardware is also an important factor influencing millimeter wave precoding.
At present, some millimeter wave communication transceiver hardware non-ideal factor modeling researches are carried out at home and abroad, but the transceiver hardware non-ideal factors have multiple aspects, and most of documents only research the influence of one aspect. Based on these unilateral transceiver hardware nonideality studies,
there are three types of hardware that have been investigated: different antenna deployment at the base station end can cause different phase noise. When the base station has sufficient power, the base station end uses a discrete local oscillator to obtain better performance than a cooperative local oscillator. The method comprises the steps of respectively taking system capacity as a quality index under the conditions of a discrete local oscillator and a cooperative local oscillator, researching how phase noise damages communication quality, analyzing traversal capacity after obtaining the instantaneous signal-to-noise ratio of a subcarrier, and then providing a phase noise tracking algorithm based on Kalman filtering, so that the damage of the phase noise to the communication quality is reduced. The SINR penalty of local oscillation can be quantified as a reduction in channel state information obtained at the base station side, compared to a scenario where local oscillation is not considered ideal, and it is found that as the oscillator increases, the SINR decreases as the interference energy increases.
Under the condition of non-ideal hardware, the influence of the non-ideal low-noise amplifier on the system performance cannot be ignored, and the low-noise amplifier in millimeter wave communication is optimized on the basis of the constant contour, so that lower noise is generated. However, a large overhead is brought by optimizing the low noise amplifier to improve the system performance, so that a method for researching the influence of the low noise amplifier on the system performance needs to be researched, and then the influence of the low noise amplifier is reduced by an appropriate precoding method. And the quality factor is taken as an index, and the influence of low-noise amplification on the system performance is modeled by combining the noise amplification coefficient.
Although there are currently many studies on millimeter wave precoding and hardware non-ideal factors, there are few studies on combining the two. On the premise of controlling cost, reducing damage of hardware to system capacity by optimizing hardware faces a plurality of problems, so that designing a reasonable precoding matrix under a non-ideal hardware condition is very important.
The performance of precoding performance is obviously influenced by non-ideal factors of devices, and the spectrum utilization rate of the non-ideal factors is obviously lower than that of precoding under an ideal hardware condition.
Disclosure of Invention
The present invention aims to improve the system capacity by optimizing the precoding matrix under the condition that the total transmission power of the base station is limited by using the idea of convex optimization aiming at the above situation. The observation index is the spectrum utilization, and in the narrow-band transmission, the system capacity can be approximated to the spectrum utilization.
In order to solve the problems, the technical scheme of the invention is as follows:
a precoding method for a millimeter wave MIMO system, the method for use in device non-ideal conditions, comprising the steps of:
s1, establishing an objective function under the hardware nonideal condition:
setting hardware non-ideal factors including local reference source noise, thermal noise caused by low noise amplification and ADC quantization noise, establishing a receiving signal y of a user k k Sum signal to interference plus noise ratio (SINR) k The model of (2) is:
wherein the content of the first and second substances,representing signals, vectors, transmitted by the base station to user kRepresenting the corresponding channel, N being the number of antennas;representing precoding matrixes corresponding to the users 1 to K; n is k Representing a mean of 0 and a variance of σ 2 Additive receive noise; recording equivalent channelsb is ADC quantization digit; after the signal passes through the ADC converter, the energy is reduced to (1-rho) ADC )|d kr h k D t w k x k | 2 (ii) a Increase the energy to rho ADC |d kr h k D t w k x k | 2 Additive quantization noise of (a); n is a radical of hydrogen F A noise amplification factor for low noise amplification;
the objective is set to maximize the system capacity under the condition that the total transmission power of the base station is limited, so that the objective function is obtained as follows:
wherein the system capacityg k (SINR k )＝log 2 (1+SINR k ) SINR is a function of the precoding matrix w;representing a precoding matrix corresponding to user k, to transmit signal x k Normalized, then | | w k || 2 ＝||w k x k || 2 The corresponding transmission power is shown, and P is the total transmission power of the base station; the problem f (. Cndot.) Lipphiz increases continuously and monotonically, g k (. DEG) continuously and strictly monotonically increases, so that the problem turns into a monotonic optimization problem, which can be searched for its optimal point with the BRB algorithm.
S2, solving the objective function provided in the step S1 by adopting a BRB search algorithm:
set of devicesIs represented by g k Because the optimal solution is on the boundary of the R, a plurality of mutually disjoint search squares M are arranged on the boundary by adopting a BRB algorithm. Suppose search square M = [ a, b =]And isThe upper and lower bounds of the optimal point in M can be obtained by a curve search method and are respectively as follows:
f min,M ＝f(n)
the strict growth curve used in the curve search is r (tau), r (0) = a, r (tau) upper )＝b，τ upper >, 0. In the above formula e k Express identity matrix I K K column of (1), [ n, m ]]For the curve search results:
continuously changing the size of the search grid in finite iterations and continuously reducing the upper bound f of the optimal point in the search grid max And a lower bound f min To approximate the boundary until satisfied
f max -f min <ε
When the corresponding solution is the optimal pointEpsilon is the allowable error. Satisfies the optimum pointThe precoding matrix w of (a) is the corresponding precoding matrix that needs to be found.
The present invention studies the downlink, considering several major hardware non-idealities as local oscillation phase noise, low noise amplifiers, and ADC quantization noise. And (3) applying the BRB search algorithm to a non-ideal hardware scene by utilizing the idea of convex optimization, and optimizing the precoding matrix under the condition that the total transmitting power of the base station is limited to finally obtain the optimal solution.
Drawings
Fig. 1 is a diagram of a BRB algorithm grid search process in the present invention.
Detailed Description
Specific embodiments of the present invention will be given below with reference to the accompanying drawings.
The objective is set to maximize the system capacity under the condition that the total transmit power of the base station is limited, so that the objective function is obtained as follows:
wherein the system capacityg k (SINR k )＝log 2 (1+SINR k ) SINR is a function of the precoding matrix w;representing the precoding matrix corresponding to user k, will transmit signal x k Normalized, then | | w k || 2 ＝||w k x k || 2 Indicates its corresponding transmission power; the problem f (. Cndot.) Lipphiz increases continuously and monotonically, g k (. DEG) continuously and strictly monotonically increases, so that the problem turns into a monotonic optimization problem, which can be searched for its optimal point with the BRB algorithm.
In order to achieve the purpose, the technical scheme of the invention is as follows: the method proposes the optimization of precoding under the non-ideal condition of hardware based on the BRB algorithm:
for any search square M, set f max = β (M) represents the upper bound of the value of the optimum point in M,representing a feasible point in M. In each iteration, the search squares are divided, and the upper and lower boundaries of the optimal point in the search squares are continuously reduced to approach the boundary until the optimal point is searched.
Set the original search grid to M initial ＝[0,b initial ]RequireGet M initial One feasible point inThen the original square grid M initial The upper and lower bounds of the value of the medium optimal point are respectively And then, entering an iterative algorithm, and considering that the optimal point is found when the constraint condition is met after finite iterations. Each iteration processes one search square, and the specific method of iteration is as follows:
s1, marking the initial grid of the iteration as M 0 ＝[a 0 ,b 0 ]Taking M 0 Middle feasible pointWill be the initial square M 0 Divided into two small squares transversely
Where dim = argmax k [b 0 -a 0 ] k ,d＝[a 0 +b 0 ] dim (2) small squaresThe upper bound of the medium optimum points is:
getMiddle feasible pointAt the moment, small checksThe lower bound of the medium optimum isWherein:
s2, small squaresRemoving parts which are unlikely to contain the optimal point, i.e. removing parts whose value is below the lower bound f min And above the upper bound f max To obtain reduced squares Given by the following theorem:
due to the objective functionIn the formula v ik ,u ik The values are as follows:
s3, confirming the reduced squareWhether a feasible solution exists. If no feasible solution exists in the reduced square grids, deleting the square grids, otherwise, additionally defining new upper and lower boundaries of the optimal point, judging whether the conditions are met, and if not, entering next circulation S1;
s4, meeting the optimal pointThe precoding matrix w is the corresponding precoding matrix that needs to be found.
Claims (1)
1. A precoding method for millimeter wave MIMO systems for maximizing system capacity under a condition that total transmission power of a base station is limited under a device non-ideal condition, comprising the steps of:
s1, establishing an objective function:
under the condition that the total transmitting power of the base station is limited, an objective function is established as follows:
wherein the system capacityg k (SINR k )＝log 2 (1+SINR k ) SINR is a function of the precoding matrix w;representing a precoding matrix corresponding to a user K, wherein K is the total number of users and N is the number of antennas, and transmitting a signal x k Normalized, then | | | w k || 2 ＝||w k x k || 2 The corresponding transmission power is shown, and P is the total transmission power of the base station; f (-) Ripphiz increases continuously and monotonically, g k (. O) continuously and strictly monotonically increases, thus the problem turns into a monotonic optimization problem;
s2, optimizing the pre-coding under the non-ideal condition of hardware by adopting a BRB algorithm: setting a plurality of mutually disjoint search squares M, and setting f for any search square M max β (M) represents the upper bound of the value of the optimum point in M,a feasible point in the M is represented,is represented by g k Through iteration, in each iteration, the search square is divided, the upper and lower bounds of the optimal point in the search square are continuously reduced to approach the boundary until the optimal point is searched outEpsilon is an allowable error;
s3, obtaining the optimal point through the step S2The precoding matrix w of is targetedA precoding matrix.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108337030A (en) * | 2018-02-07 | 2018-07-27 | 东南大学 | High effect beam-forming method, device and equipment in multiaerial system |
| CN112702754A (en) * | 2020-12-25 | 2021-04-23 | 南方电网电力科技股份有限公司 | Data secure transmission method, device, equipment and storage medium |
| CN113315556A (en) * | 2021-05-28 | 2021-08-27 | 西安电子科技大学 | Two-stage power distribution method suitable for compact array Massive MIMO system |
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| US20140016573A1 (en) * | 2012-07-12 | 2014-01-16 | Samsung Electronics Co., Ltd. | Apparatus and method for random access with multiple antennas in a wireless network |
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