Antenna Selection Algorithm Research Articles

Improving physical layer security in distributed multiple‐input multiple‐output dual‐function radar‐communication systems

AbstractA distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI.

Key technologies of smart antenna in WLAN based on adaptive array

In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity.

Multi-Array Visible-Light Optical Generalized Spatial Multiplexing–Multiple Input Multiple-Output System with Pearson Coefficient-Based Antenna Selection

To address the limitations of poor environmental adaptability, unsatisfactory bit-error performance, and high complexity of conventional antenna selection algorithms applied to a multi-array visible-light optical generalized spatial multiplexing–multiple-input multiple-output (OGSMP-MIMO) system, an OGSMP-MIMO system based on Pearson coefficient antenna selection is proposed. The algorithm adopts the correlation of Pearson coefficients among photoelectric detector (PD) terminals at different positions and active transmit antennas to select the optimal antenna combination without relying on the accuracy of channel estimation, for realizing the multiplexing of the time and space domains, and to improve the bit-error performance. Finally, experiments were conducted to verify the feasibility of the antenna selection algorithm, based on the Pearson coefficients. The results indicated that when the bit-error rate reached 10−6, using the antenna selection algorithm based on the Pearson coefficient, the signal-to-noise ratio was improved by 2.7 dB and 3.7 dB when compared with the norm-based antenna and random selection algorithms, respectively. In addition, increasing the number of active transmitting antennas can improve the transmission rate; however, the bit-error performance will be compromised. In the same modulation mode, increasing the number of transmitting antennas will reduce the bit-error performance.

Antenna Selection for Reconfigurable Intelligent Surfaces: A Transceiver-Agnostic Passive Beamforming Configuration

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/full-RF and multiplexing-/diversity-oriented setups.

Antenna Selections Strategies for Massive MIMO Systems With Limited-Resolution ADCs/DACs

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

Transmit Antenna Selection for Sum-Rate Maximization with Multiclass Scalable Gaussian Process Classification

Antenna selection techniques are extensively applied to reduce hardware cost and power consumption in multiple-input multiple-output (MIMO) systems. This paper proposed a low-cost antenna selection method for system sum-rate maximization based on multiclass scalable Gaussian process classification (SGPC) which is capable to perform analytical inference and is scalable for massive data. Simulation results show that the average sum-rate obtained by SGPC is 1. 9 bps/Hz more than that obtained by conventional optimization driven user-centric antenna selection (UCAS) algorithm and 1 bps/Hz more than that obtained by the up-to-date learning scheme based on a deep neural network (DNN) when signal-to-noise ratio (SNR) is 10 dB, the number of total antennas at BS is 6, the number of selected antennas is 4, and the number of single-antenna users is 4. The superiority of SGPC over UCAS and DNN is more obvious as SNR, the number of selected antennas, or the number of users increases.

Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment

With the development of the Internet of Things (IoT), more and more devices are connected in edge-cloud environment, and spectrum scarcity has become a major bottleneck for the employment of IoT devices in the end side of cloud computing. In addition, due to the broadcast nature of RF link, the potential eavesdropper may be presented in the wireless environment. To improve spectrum efficiency (SE) and ensure safe transmission of the users, in this paper, the two-way relay cooperative Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) with massive multiple-in multiple-out (MIMO) is proposed. Our objective is to maximize the security energy efficiency (SEE) of the cognitive system subject to the quality of service (QoS) of users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e., the optimization of transmit power, power allocation, and antenna selection, respectively. The Lagrange dual algorithm based on the first-order Taylor series expansion function is proposed to solve the non-convex problem. Moreover, a novel orthogonal antenna selection algorithm is proposed to decrease the cost of radio frequency chains and limit the interference to the primary user. The simulation results show that the proposed scheme has significant performance gain on SEE. It can effectively increase the number of access for cloud computing edge IoT users. In addition, due to the impact of correlation between the antennas, as the number of relay antennas increases, the SEE converges to a specific value, which exists the optimal number of antenna.

An antenna subset selection algorithm in distributed MIMO radar for target localization via convolutional neural network

AbstractSince the distributed MIMO radar (DMR) has widely spread transmitters and receivers, it can provide higher target detection probability, as well as superior target tracking and localization performance than the monostatic/bistatic radar systems. An effective radar resource allocation scheme can optimise the DMR system parameter and obtain better system performance. In this paper, a critical but limited system resource is optimised, that is, select a subset of the active radar antennas. In this scenario, the convolutional neural network of the antenna subset selection for target localization (LCNN‐ASS) algorithm in the DMR is proposed based on two free switching policies. The proposed algorithm is immune to the failure of a single policy and selects antenna subsets from the entire sets with a remarkable computational speed. Therefore, the proposed algorithm increases the flexibility of resource scheduling over traditional algorithms. Simulation experiments and performance analysis demonstrate the localization performance and flexibility of the LCNN‐ASS algorithm.

Improved Butterfly Optimization Algorithm for Energy Efficient Antenna Selection Over Wireless Cellular Networks

Improved Butterfly Optimization Algorithm for Energy Efficient Antenna Selection Over Wireless Cellular Networks

Joint Energy Harvesting and Transmission Optimization for Cell-Free Massive MIMO With Network-Assisted Full Duplexing

This paper investigates the problem of joint energy harvesting (EH) and transmission optimization for cell-free massive MIMO with network-assisted full duplexing (NAFD), where each access point (AP) works in full-duplex mode, half-duplex mode or another flexible mode and is connected to a central processing unit (CPU) via compressed fronthaul links. The high demand on the fronthaul links and series cross-link interference (CLI) are the two main problems in cell-free massive MIMO with NAFD. Therefore, in our scheme, we propose a joint strategy of EH, fronthaul compression, and CLI cancelation to maximize the energy efficiency (EE) of the system under constraints of guaranteed quality-of-service (QoS), EH and compressed fronthaul requirements. A two-stage strategy of EH and transceiver design is proposed to solve the complicated optimization problem. Specifically, in the first stage, we propose an EH/information receiving (IR) antenna selection algorithm to design the operation mode of the antennas for uplink users and receiving APs. In the second stage, with the antennas selected in the first stage, we further attempt to solve the EE maximization problem. Furthermore, to solve the highly nonconvex EE maximization problem, we propose a fractional programming (FP)-based two-tier iterative algorithm in which a series of nonconvex–convex approximation methods, such as FP, iterative convex approximation and equivalent formulation, are used. Simulation results demonstrate that the proposed algorithms yield a higher EE gain than the traditional time-division duplexing (TDD) and co-frequency co-time full duplexing (CCFD) schemes.

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.

A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms

A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology. The effectiveness of the simulated BAB algorithm is evaluated based on channel capacity and compared with the traditional state of arts such as fast antenna selection algorithm, Exhaustive Search, Fast antenna selection, CBF, CBW, Random antenna selection, etc. Sunflower Optimization-based antenna selection has been shown to provide improved results in terms of channel capacity when compared to the traditional Branch and Bound algorithm. The results indicate that the Sunflower Optimization technique is a promising alternative for antenna selection in Massive MIMO systems, especially in cases where a large number of antennas are present at the transmitter and receiver ends. The proposed solution provides significant improvements over the traditional methods, making it an attractive option for optimizing MIMO performance in future wireless communication systems.

Antenna Selection in Switch-Based MIMO Arrays via DOA Threshold Region Approximation

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.

Full-Duplex Buffer-Aided MIMO Relaying Networks: Joint Antenna Selection and Rate Allocation Based on Buffer Status

The in-band full-duplex buffer-aided relaying is a spectral-efficient technology for extending the range of reliable communication. In this context, we introduce a novel joint buffer-aware rate allocation and antenna selection algorithm to schedule the network transmissions. The closed-form expression for the average throughput of the network is derived under Nakagami-m fading environment. Through Monte-Carlo simulations, we demonstrate that our proposed buffer-aware approach significantly outperforms the conventional schemes with prefixed antenna allocation considering two benchmarks operating based on the receive-antenna-selection (RAS)/transmit antenna selection (TAS) and maximum-ratio-combining (MRC)/TAS. The overlapping results in the presented simulation examples corroborates our theoretical analysis.

Performance Analysis of a Novel Optimal Antenna Selection Algorithm for Large MIMO

LTE MIMO is capable of providing some major enhancements in spectral efficiency and performance along with adding complexity to the system. One way to make sure that the sent data has reached the receiver end is to increase the number of antennas between them. But when the quantities of antennas are increased, there is increase in the probability that deep fading is experienced by at least some antennas. This results in giving out some undesirable outcomes which affects the overall efficiency of the MIMO system. To handle these issues, a reliable technique has been presented that involves selection of antenna subset. The proposed technique incorporates combination of SBO and PSO for antenna selection. The maximum channel capacity of the channel has been considered as the objective function for selecting optimal antennas. The comparison of the proposed approach’s performance and the existing approaches’ performance is done in terms of BER, energy efficiency, spectral efficiency and optimal transmit power.

Cluster-Based Multi-Carrier Hybrid Beamforming for Massive Device Terahertz Communications

We propose a cluster-based multi-carrier beam division multiple access (MC-BDMA) scheme to enable massive device terahertz (THz) communications with the dynamic-subarray hybrid beamforming (HBF) architecture. This scheme is motivated by a limitation of conventional HBF architectures, i.e., the number of users cannot exceed the number of radio frequency (RF) chains. By exploiting the unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, we propose multiple users in the same cluster to be supported by a single RF chain with distance-aware multi-carrier modulation. Moreover, we propose different clusters to be divided by BDMA that is accomplished through HBF design. In our proposed scheme, we first design a novel antenna selection and subarray partition algorithm for the dynamic-subarray HBF architecture to compensate for performance loss caused by diverse channels among users. We then design an algorithm for multi-carrier HBF which maximizes the achievable throughput and eliminates the inter-beam interference. Numerical results show that our proposed cluster-based MC-BDMA scheme with the dynamic-subarray HBF architecture provides an almost 100% spectral efficiency gain and a 60% energy efficiency gain over the conventional non-cluster HBF scheme in massive device communications.

An ℓ0-norm-constrained adaptive algorithm for joint beamforming and antenna selection

This paper presents a new adaptive algorithm for joint beamforming and antenna selection in mobile communication systems. Such an algorithm is of particular interest for massive multiple-input multiple-output (mMIMO) antenna systems along with limited number of available radio-frequency chains. The proposed algorithm is based on introducing an ℓ0-norm constraint to an established adaptive-projection beamforming optimization scheme. In doing so, a real-time beamforming optimization can be carried out, resulting in a beamforming vector that tends to be sparse. The higher-magnitude elements of such a vector point out to the antenna-array elements that have the largest contribution to the output signal-to-interference-plus-noise ratio (SINR). Consequently, an effective antenna selection can be achieved directly by considering the magnitude of the beamforming coefficients. Simulation results confirm the effectiveness of the proposed algorithm in enhancing the output SINR.

Manifold Learning Inspired Dynamic Hybrid Precoding With Antenna Partitioning Algorithm for Dual-Hop Hybrid FSO-RF Systems

The decode-and-forward (DF) based free-space optical-radio frequency (FSO-RF) hybrid system combines the advantages of both FSO links and RF links, which makes the system easy to be deployed and enables the extended transmission coverage. In the case of multiple users, the unreasonable dynamic antenna selection algorithm will lead to multi-user interference (MUI) and reduce the spectral efficiency of the system. Therefore, we propose hybrid precoding based on manifold learning with the antenna partitioning algorithm for dual-hop hybrid FSO-RF systems. In the hybrid precoding of dynamic subarray structure, the high-dimensional channels are embedded into the low-dimensional manifolds by the optimized multidimensional scaling (MDS). The potential spatial correlations of the high-dimensional channel are preserved by the scaling by majorizing a complicated function (SMACOF) algorithm. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. Meanwhile, an antenna subarray partitioning algorithm is proposed, so that each antenna unit can be assigned to an RF chain based on the increment of the user’s maximum signal to interference noise ratio (SINR). By calculating the simulated equivalent channel SINR for the selected users, the antenna division can greatly reduce the computational complexity and the size of the search space, and ensure fairness among users. Simulation results show that this method can obtain almost the best summation rate and higher spectral efficiency compared with the conventional method.

Study of the Impact of Antenna Selection Algorithms of Massive MIMO on Capacity and Energy Efficiency In 5G Communication Systems

Massive MIMO system in the fifth generation can consume a large amount of energy. In this research, the selection of antennas was studied based on several algorithms and a new algorithm was proposed for the selection of antennas. The results showed through a comparison between the approved and proposed methods in the research in terms of capacitance that The proposed algorithm is the closest in terms of capacity to the ideal case, as this method is considered almost ideal for its application in multi-input and multi-output systems to improve performance in the fifth generation, followed by the greedy algorithm and algorithm norm, as it was noted from During the comparison in terms of energy efficiency, it is possible to increase energy efficiency when choosing a certain number of antennas

An Optimal Selection Algorithm Based on Q-Learning For Dual Media Communication System With Full-Duplex Relays

Full-duplex communication technology can make full use of spectrum resources through simultaneous co-frequency transmission, but the interference of the signal sent by the device itself degrades performance. The paper proposes a joint antenna and relay selection algorithm based on Q-learning, which selects the best relay with the best transmit-receive configuration for the communication between the source and the target. Specifically, by learning the dual-medium cooperative communication environment to obtain feedback in terms of interruption probability and channel capacity, so as to determine the best relay and antenna. Finally, the paper verifies the effectiveness of the algorithm by simulation.