Optimal Antenna Selection Research Articles

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.

Joint Design of Antenna Selection and Resource Allocation in Massive MIMO‐Based Ultra Dense Heterogeneous Networks

Fifth generation (5G) communication network is anticipated to satisfy the need for higher data rates, less latency, ubiquitous connectivity with minimum consumption of energy and acceptable quality of Service (QoS). The massive MIMO and heterogeneous network (HetNet) has evolved as a promising technology to address the aforementioned challenges. In the proposed framework, the massive MIMO technology at the macro base station and full duplex (FD) technology enabled at the small cell base station in HetNet are investigated. An optimal antenna selection strategy, user association (UA), and power allocation (PA) in a massive MIMO‐based HetNet with FD‐enabled small cells are proposed to optimize system sum rate. Initially, the objective function of the optimization problem is nonconvex and a mixed integer nonlinear programming problem. Then, the solution is obtained by transforming the problem into convex problem utilizing Lagrangian decomposition. Simulation results manifest the effect of the number of base station antennas, number of small cell base stations, self‐interference cancellation factor, and channel state information on system sum rate. Moreover, it is evident that the propounded algorithm is effective in terms of optimizing the system sum rate when compared to the conventional maximum signal‐ to ‐ interference ‐ plus ‐noise ‐ ratio (SINR) algorithm.

Optimal Antenna Selection and Beamforming for an IRS Assisted System

An intelligent reflecting surface (IRS) is a cost and energy-efficient solution to improve wireless system performance. Transmit antenna selection (AS) harnesses the benefits of multiple antennas with a smaller number of radio frequency (RF) chains. We focus on joint optimization of antenna subset and transmit beamforming at the transmitter (Tx) and passive beamforming at the IRS to maximize the receive signal power. We derive a closed-form optimal AS rule for a Tx and receiver (Rx) equipped with single RF chain each and ideal IRS. We analyze its performance with a correlated channel model and then extend it to non-ideal IRS. We also propose a simpler rule that significantly reduces the number of computations and pilots. For an Rx that performs maximal ratio combining, we propose a manifold optimization algorithm and a low-complexity selection rule. For a Tx with multiple RF chains, we propose a subset selection algorithm that yields a locally optimal solution and an alternating optimization algorithm that reduces complexity. Our simulations study the impact of estimation errors, discrete phase shifts, and channel correlation on the proposed selection rules, which perform better than the existing AS rules. They also show that the proposed low-complexity rules are near-optimal.

Distributional Spectrum Series (DSS) with Beamforming in 5G Communication System

The transmission channel choice and other relevant characteristics affect the communication system's bandwidth loss and signal strength. This causes energy loss during transmission since the channel bandwidth is being used up so heavily. This may be avoided by carefully choosing the channels in the spectrum to allot the bandwidth in accordance with the consumption limit. In this study, the optimum energy reduction-based 5G communication system aids in improving the channel bandwidth selection in accordance with the prediction of the ideal antenna directionality. The Distributional Spectrum Series (DSS) and the beamforming method may be used to accomplish this. This kind of optimum antenna selection analyzes the signal spectrum and optimizes the energy based on the beamforming parameters by using directionality prediction from the Direction of Arrival (DOA). The testing results and comparative graphs demonstrate the performance of the proposed 5G communication system through parameters of Energy (Joule), Blocking Probability (BP), throughput (bps), and other relevant factors.

Real-Time CRLB Based Antenna Selection in Planar Antenna Arrays

Estimation of User Terminals’ (UTs’) Angle of Arrival (AoA) plays a significant role in the next generation of wireless systems. Due to high demands, energy efficiency concerns, and scarcity of available resources, it is pivotal how these resources are used. Installed antennas and their corresponding hardware at the Base Station (BS) are of these resources. In this paper, we address the problem of antenna selection to minimize Cramer-Rao Lower Bound (CRLB) of a planar antenna array when fewer antennas than total available antennas have to be used for a UT. First, the optimal antenna selection strategy to minimize the expected CRLB is proposed. Then, using this strategy as a preliminary step, we present a two-stage greedy antenna selection method whose goal is to minimize the instantaneous CRLB. The optimal start point of the greedy algorithm is presented alongside some methods to reduce the algorithm’s computational complexity. Numerical results confirm the accuracy of proposed solutions. They demonstrate that the proposed antenna selection method only requires a small proportion of the total available antennas to accomplish a significant amount of the total performance, enhancing hardware utilization efficiency. Also, it is shown that the presented algorithm has a high error tolerance.

Secure MIMO systems with nonlinear energy harvesting using optimal transmit antenna selection over [formula omitted] fading channels

This paper investigates the secrecy performance of multiple-input multiple-output systems with a nonlinear energy harvesting (EH) scheme used at the transmitter to improve energy efficiency and extend the lifetime of energy-constrained systems. Most of the current literature investigates cognitive radio networks with linear EH model. However, in order to realize more realistic scenarios due to hardware constraints, such as the saturation and sensitivity characteristics of the EH components, a nonlinear EH model is examined here and compared with a simpler linear EH scheme. In particular, a practical nonlinear energy harvester model with a source, a destination, an active eavesdropper, and a power beacon is considered under generalized α−μ fading conditions. Here, the eavesdropper can overhear the data transmission messages when the source communicates with the destination using wiretap channels. Furthermore, the source employs all its antennas to combine the radio frequency signals emitted by the power beacon via the maximal ratio combining (MRC) scheme. Moreover, the MRC scheme is used at the destination and eavesdropper to improve the signal’s quality (i.e., at the receiver side). Closed-form expressions for the lower bound and asymptotic security performance are investigated. Next, optimal antenna selection (OAS) scheme and traditional space–time transmission (STT) scheme are compared based on whether the channel state information (CSI) of the wiretap link is available. In addition to deriving the secrecy diversity order and secrecy array gain, asymptotic closed-form analytical expressions for security performance and closed-form analytical expressions for the probability of strictly positive secrecy capacity are also derived for various selection schemes. Finally, numerical results indicate that, compared to the linear EH scheme with just minimal trade-offs, the nonlinear EH scheme represents more realistic and dependable outcomes.

Energy efficient power allocation with joint antenna and user selection in massive MIMO systems

The problem of maximizing energy efficiency (EE) is considered in this paper. The system in question is a massive multiple-input multiple-output (MIMO) system that transmits information simultaneously in the uplink and downlink. To maximize EE, an optimal antenna selection strategy is used to send information. Furthermore, we propose an optimal user selection method and then obtain their optimal allocated power. The optimization problem of the system has two constraints, including power limitations in the uplink and downlink. Due to the non-convexity and being constrained of the optimization problem, in order to obtain the optimal power of users, it must first be converted to a convex problem form and then turned into an unconstrained problem using the Lagrange dual function method. The bisection algorithm is utilized to select the optimal set of active antennas for sending information. The cross-entropy algorithm with the proposed algorithm is used simultaneously to select the set of users and determine their optimal power values. The results of computer simulations show that the proposed selection schemes of antennas and users significantly improve the system performance.

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.

Hybrid Meta-Heuristic Algorithms Based Optimal Antenna Selection for Large Scale MIMO in LTE Network

MIMO is a type of antenna technology which utilizes multiple antennas to separate signals traveling in various paths as a result of reflections, etc., to be separated and their ability utilized to enhance the throughput of data and the signal to noise ratio which in return enhances the performance of the system. For providing enhanced signal performance and enhanced data rates, MIMO is utilized within LTE. When the quantities of antennas are increased, there is increase in the probability that deep fading is experienced by at least some antennas, 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. Antenna selection’s main concept is to utilize a bounded quantity of analog chains that are adaptively switched to subset of the available antennas capable of preserving the selection diversity gains and also can minimize the quantity of radio frequency chains needed. 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. From the simulation result, it has been shown that the presented approach’s performance has been better than the performance of the existing approaches in terms of BER, energy efficiency, spectral efficiency and optimal transmit power.

Security Performance Analysis of Relay Networks Based on κ - μ Shadowed Channels with RHIs and CEEs

This paper mainly describes the transmission reliability and physical layer security of multiantenna decode-and-forward relay networks over the κ - μ shadowed fading distribution. We consider the existence of residual hardware impairments (RHIs) and channel estimation errors (CEEs) at the same time. Firstly, we give the network model. On this basis, we analyze the reliability and security by deriving the exact expressions of outage probability (OP) and intercept probability (IP). To further verify and investigate the theoretical derivation, the approximate analysis of OP is obtained under the high signal-to-noise ratio. In addition, we also use an optimized antenna selection technology at the transmitter to improve the security performance. Finally, through the comparative study of theoretical simulation and Monte Carlo simulation results, the factors affecting network transmission and security are acquired. The interesting conclusion is that both RHIs and CEEs can increase OP and decrease IP; that is to say, the existence of RHIs and CEEs can reduce reliability and enhance security.

A POMDP-Based Antenna Selection for Massive MIMO Communication

We use a partially observable Markov decision process (POMDP) framework to design an optimal antenna selection policy for downlink transmit beamforming at a multi-antenna base station (BS) equipped with only a limited number of RF chains. Assuming that the channel state evolves according to a finite-state Markov process and that only the channel coefficients which correspond to previously selected antennas, are available at the BS, we use the POMDP framework for antenna selection with the aim to maximize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term expected downlink data rate</i> . To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy and prove that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in the case of positively correlated two-state Markov model for the channel over each antenna, the myopic policy is optimal for antenna selection for any number of RF chains</i> . Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-state coarse channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which uses full channel state information without quantization.

Hardware Efficient Massive MIMO Systems with Optimal Antenna Selection.

An increase in the number of transmit antennas (M) poses an equivalent rise in the number of Radio Frequency (RF) chains associated with each antenna element, particularly in digital beamforming. The chain exhibits a substantial amount of power consumption accordingly. Hence, to alleviate such problems, one of the potential solutions is to reduce the number of RFs or to minimize their power consumption. In this paper, low-resolution Digital to Analogue Conversion (DAC) and transmit antenna selection at the downlink are evaluated to favour reducing the total power consumption and achieving energy efficiency in mMIMO with reasonable complexity. Antenna selection and low-resolution DAC techniques are proposed to leverage massive MIMO systems in free space and Close In (CI) path-loss models. The simulation results show that the power consumption decreases with antenna selection and low-resolution DAC. Then, the system achieves more energy efficiency than without low-resolution of DAC and full array utilization.

Transmit antenna selection strategies for spectrally efficient spatial modulation techniques

SummaryFifth‐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.

An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

SummaryThe massive multiple‐input multiple‐output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and transmit antenna selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA and the TAS of wireless communication in millimeter‐wave (mm‐wave) with massive MIMO technology are considered. Two different solutions are developed for this research such as the deep learning method for efficient resource allocation process and optimization algorithm for TAS process. Here, the RA process is done with the help of attention‐based capsule auto‐encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, battle royale optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipment (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances spectral efficiency (SE), energy efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE, and SE.

A novel massive MIMO strategy for optimal antenna selection via hybrid algorithm

ABSTRACT One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively.

NOMA with optimal transmit antenna selection scheme with self adaptive metaheuristic model

AbstractNonorthogonal multiple access (NOMA) constitutes a promising technique in the fifth‐generation (5G) mobile networks. On the transmitter side, the concurrent usage of multiple antennas needs an appropriate count of parallel Radio Frequency (RF) chains at the front end. Owing to this consequence, it maximizes the power consumption, cost, and system complexity along with maximization in the antenna count. In order to tackle these aforesaid problems, some researchers prefer the Transmit Antenna Selection (TAS) for modeling the transmitter structure simple. The best antenna is selected opportunistically by an efficient TAS technique from multiple antennas and deploys a single RF chain for transmission. In this paper, a novel approach for optimized antenna selection in overlay CR networks using NOMA and SM is proposed via the usage of optimization tactics. To choose the desired respective antenna, this paper introduces a new Mean and Median Fitness Centered Moth flame Optimization (MM‐FCMO), which is the modified Moth Flame Optimization (MFO) Algorithm, which taken the core objectives such as power consumption and Mean Square Error (MSE). The MSE and power consumption of the chosen antennas have to be lower for the system to attain maximum antenna gain with lower noise. Finally, to exhibit that the presented work is much significant than the traditional works, a comparative analysis is made in terms of error minimization and statistical evaluation.

Evaluation of a Switched Combining Based Distributed Antenna System (DAS) for Pedestrian-to-Vehicle Communications

The safety of vulnerable road users is paramount, particularly as we move towards the widespread adoption of autonomous and self-driving vehicles. In this study, we investigate the use of a six-element distributed antenna system (DAS), operating at 5.8 GHz and mounted on the exterior (i.e., roof and wing mirrors) of an automobile, to enhance signal reliability for pedestrian-to-vehicle (P2V) communications. Due to its low complexity and ease of implementation, we consider the use of a switch-and-examine combining with post-examining selection (SECps) scheme to combine the signal received by the DAS. During our experiments, a pedestrian wearing a wireless device on their chest either stood stationary or walked by the side of a road. It was found that the overall signal reliability depends on not only the number, but also different groupings of the antennas which are selected. The goodness-of-fit results have shown that the temporal behavior of the diversity gain was adequately described by the Gaussian distribution. Building upon this, we also provide some useful insights into the antenna selection through the comparison of three different antenna selection mechanisms, namely per-sample random antenna selection, one-shot antenna selection and per-sample optimal antenna selection.

Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

Greedy Search-Based Optimal Transmit Antenna Selection for Massive MIMOME Channels

The conventional transmit antenna selection in massive multiple-input multiple-output multiple antenna eavesdropper (MIMOME) channels has been focused on the optimal transmit antenna selection corresponding to a given number of transmit antennas to select. To maximize the secrecy capacity, the antenna selection should be optimized considering the number of transmit antennas to select. In this letter, we propose the greedy search based optimal transmit antenna selection (G-OTAS) algorithm to maximize the secrecy capacity by pursuing the optimal number of transmit antennas in massive MIMOME channels. Throughout the simulation and numerical results, it is demonstrated that the proposed G-OTAS algorithm maximizes the secrecy capacity and effectively reduces the computational complexity compared to those of the reference schemes.

Joint Initial Uplink Synchronization and Interference Aware Resource Allocation for D2D Communication in the MU-OFDMA System

ABSTRACT Over recent epoch, initial uplink synchronization has become an essential part of the wireless communication systems owing to its amenities in the identification of new users to originate communication. Yet, allocating and sharing resources to users is a bottleneck due to the interference caused by multiple users. Besides, preceding works didn't concentrate on the usage of an effective mechanism to estimate the parameters of the received signal in the multiuser synchronization environment. These downsides affect the throughput and spectrum efficiency of the designed OFDMA system. To address these bottlenecks, this paper proposes the Joint Initial Uplink Synchronization and Interference Aware Resource Allocation (JIUS-IRA) in the Multi-User OFDMA system. JIUS-IRA comprises four processes: Parameter Estimation, Optimum Antenna Selection, Resource Allocation, and Resource Sharing. Initially, JIUS-IRA executes the Fast Block Least Mean Square-based Parameters Estimation Scheme (FBLMS-PES) to estimate the user parameters. To reduce the power consumption during transmission between 5G MIMO base station and users, JIUS-IRA selects the optimal antenna for transmission. The Sign Rank Oriented Resource Allocation (SO-RA) method is executed to allocate sub-channel resources to the cellular user. The resource requirement of D2D users present in the OFDMA system is resolved via the Harris Hawks Optimizer based Resource Sharing Scheme (H2ORSS). The numerical results acquired from the simulation are highly confidential with regard to metrics Root Mean Square Error (RMSE) of timing estimation (∼0.006), RMSE of power estimation (∼0.0079), RMSE of frequency estimation (∼ 0.008), Throughput (∼95%), Spectrum Efficiency (∼11.9 bps), and Complexity (0.045 × 104).