Antenna selection strategy for transmit beamforming-based joint radar-communication system

Ahmed, Ammar; Zhang, Shuimei; Zhang, Yimin D.

doi:10.1016/j.dsp.2020.102768

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…This helps to steer the signals in different desired directions. 48 This solution has low computational complexity compared to l 2 norm-based antenna selection. The simulation results prove that this scheme reduces the number of antennas required, while meeting the radar communication objectives.…”

Section: F I G U R E 1 Spectral Efficiency (Bpcu) Versus Number Of Tr...mentioning

confidence: 99%

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Transmit antenna selection strategies for spectrally efficient spatial modulation techniques

Gudla

Kumaravelu

Asha

et al. 2022

Int J Communication

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Summary Fifth‐generation (5G) networks are expected to offer unprecedented amounts of spectral and energy efficiency. Spatial modulation (SM) techniques have the capability to achieve an appealing trade‐off between these conflicting design parameters. Fully generalized SM (FGSM) and fully quadrature SM (FQSM) are the recent high‐rate SM variants, where the spectral efficiency is linearly proportional to the number of transmit antennas. The transmit antenna selection schemes can efficiently improve the average bit error rate (ABER) performance of SM techniques. The main objective of this work is to investigate the employment of transmit antenna selection schemes to FGSM and FQSM. Initially, Euclidean distance optimized antenna selection (EDAS) scheme is employed to FGSM and FQSM. It offers superior performance than conventional FGSM/FQSM without transmit antenna selection at the cost of higher computational complexity. In order to reduce the complexity cost, four suboptimal schemes based on channel capacity, correlation and combination of them are proposed and employed to FGSM and FQSM. The suboptimal transmit antenna selection based on capacity and correlation (TAS‐A‐C) offers a minimum gain of ∼6 dB over conventional FGSM/FQSM with significantly lower complexity. In addition, the performance of all schemes is investigated for increased number of available transmit antennas. As the proposed schemes address the spectral and energy efficiency trade‐off effectively, while corroborating better ABER performance, they have the potential to become a competing candidate for next‐generation networks.

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Section: F I G U R E 1 Spectral Efficiency (Bpcu) Versus Number Of Tr...mentioning

confidence: 99%

“…A re‐weighted l 1 norm‐based antenna selection is employed for joint radar communication. This helps to steer the signals in different desired directions 48 . This solution has low computational complexity compared to l 2 norm‐based antenna selection.…”

Section: Introductionmentioning

confidence: 99%

Transmit antenna selection strategies for spectrally efficient spatial modulation techniques

Gudla

Kumaravelu

Asha

et al. 2022

Int J Communication

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“…Following the idea in [5], the row-0 function can be approximated by a group sparsity-inducing norm, namely, the ∞,1 norm, i.e., W row-0 [38]. Similar ideas were used in [10]. Such continuous approximations allow the use of standard nonlinear program techniques to tackle (8).…”

Section: Joint (R)bfandas: Existing Approachesmentioning

confidence: 99%

“…The vast majority of the literature tackles this problem using continuous programming-based approximations. For example, [5]- [7], [10] used convex and nonconvex group sparsity-promoting regularization to encourage turning off antenna elements. However, the continuous approximations are often NP-hard problems as well (especially when the sparsity promotion is done via nonconvex quasi-norms as in [5]), and thus it is unclear if they can solve the problem of interest optimally.…”

mentioning

confidence: 99%

“…Our design leverages problem structures of unicast BF and RBF, which allows for branching only on a subset of the optimization variables-thereby having reduced complexity and being effective. Unlike continuous optimization-based approximations in [5]- [7], [10] whose solutions are often suboptimal or infeasible, the proposed B&B is guaranteed to return an optimal solution. • An ML-based Acceleration Scheme.…”

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confidence: 99%

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Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

Shrestha¹,

Xiao²,

Mei³

2022

Preprint

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This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). Such problems arise in scenarios where the number of the radio frequency (RF) chains is smaller than that of the antenna elements at the transmitter, which has become a critical consideration in the era of large-scale arrays. The joint (R)BF&AS problem is a mixed integer and nonlinear program, and thus finding optimal solutions is often costly, if not outright impossible. The vast majority of the prior works tackled these problems using continuous optimization-based approximations-yet these approximations do not ensure optimality or even feasibility of the solutions. The main contribution of this work is threefold. First, an effective branch and bound (B&B) framework for solving the problems of interest is proposed. Leveraging existing BF and RBF solvers, it is shown that the B&B framework guarantees global optimality of the considered problems. Second, to expedite the potentially costly B&B algorithm, a machine learning (ML)-based scheme is proposed to help skip intermediate states of the B&B search tree. The learning model features a graph neural network (GNN)-based design that is resilient to a commonly encountered challenge in wireless communications, namely, the change of problem size (e.g., the number of users) across the training and test stages. Third, comprehensive performance characterizations are presented, showing that the GNN-based method retains the global optimality of B&B with provably reduced complexity, under reasonable conditions. Numerical simulations also show that the ML-based acceleration can often achieve an order-ofmagnitude speedup relative to B&B.

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Optimized Resource Allocation for Joint Radar‐Communications

Ahmed,

Zhang

2024

Signal Processing for Joint Radar Communications

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Antenna selection strategy for transmit beamforming-based joint radar-communication system

Cited by 21 publications

References 22 publications

Transmit antenna selection strategies for spectrally efficient spatial modulation techniques

Transmit antenna selection strategies for spectrally efficient spatial modulation techniques

Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

Optimized Resource Allocation for Joint Radar‐Communications

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