Hybrid distributed online and alternating convex optimization‐based initial value accelerated hybrid precoding for millimeter wave multiple‐input‐multiple‐output

Summary The hybrid precoding problem is considered a Frobenius norm reduction problem for the narrowband channel in a millimeter wave (mmWave) with multiple inputs and outputs (mmWave MIMO). This work proposes a hybrid distributed online and alternating convex optimization (HDO‐ACO) algorithm to imp...

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Vydané v:International journal of communication systems Ročník 37; číslo 3
Hlavní autori: Kadiyala, Ragodaya Deepthi, Kakarla, Deepti, Chikyal, Neetu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Wiley Subscription Services, Inc 01.02.2024
Predmet:
Algorithms
Ant colony optimization
Bit error rate
Convergence
Convex analysis
convex optimization algorithm
Convexity
Hermitian matrix
Heuristic methods
initial value acceleration
Lagrangian method
Millimeter waves
Narrowband
Optimization
semi‐definite programming
ISSN:1074-5351, 1099-1131
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Shrnutí:Summary The hybrid precoding problem is considered a Frobenius norm reduction problem for the narrowband channel in a millimeter wave (mmWave) with multiple inputs and outputs (mmWave MIMO). This work proposes a hybrid distributed online and alternating convex optimization (HDO‐ACO) algorithm to improve hybrid precoding (HP), interpreted as a trace minimization problem. HDO‐ACO alternately determines the digital and analog precoders to reduce the trace by keeping the other constant. Initially, HDO‐ACO uses Lagrange's method to determine the digital precoding subproblem. Then, it uses the integrated distributed online convex optimization and alternating minimization algorithm in the analog precoder design. In the HP method, the digital and analog precoders are iteratively updated until the highest number of iterations or convergence is reached. But this hybrid precoding method requires an initial analog precoding matrix input to begin the iteration. The algorithms converge gradually and fall into a suboptimal solution when the initial analog precoding matrix is set randomly. Hence, an initial value acceleration‐based heuristic approach is used in the HDO‐ACO alternating minimization algorithm that calculates the initial feasible value of an alternating minimization method using channel conditions. The simulation results of the proposed HDO‐ACO algorithm are presented under bit error rate (BER), spectral efficiency (SE), and convergence behavior by comparing it with modified block coordinate descent–HP (MBCD‐HP), manifold optimization–alternating minimization (MO‐AltMin), and semi‐definite relaxation‐based alternating optimization (SDR‐AO). The proposed HDO‐ACO attains maximum SE and less BER than other hyper‐precoding designs. A new hybrid distributed online and alternating convex optimization (HDO‐ACO) algorithm is proposed to solve the problem of hybrid precoding (HP) by interpreting it as a trace minimization problem. The digital and analog decoding problems are solved using Lagrange's method and distributed onlineconvex (DOC) algorithm, respectively. The HDO‐ACO is integrated with heuristic algorithm to speed up convergence by calculating the initial value of an analog precoder.
Bibliografia:ObjectType-Article-1
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ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5660