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The performance for communication in a real-time mobile communication system is contingent upon the distribution of spectrum among the devices and the best antenna selection, which is determined by the Direction of Arrival and the power spectrum of the antenna characteristics. The prediction and directivity of signal spectrum level based on antenna parameters were the primary areas of research attention. Generally speaking, a number of techniques anticipate the antenna spectrum level by comparing the parameters of the channel capacity and the device communication signal strength. The best selection model for the communication antenna parameter's spectrum range was used to process this. In this case, improving mobile communication performance for the entire system is thought to be the primary goal of the study. To analyze the effectiveness of antenna optimization and its performance, this has been done by testing the beamforming method's parameter under a variety of scenarios. The Boundary Assisted Antenna Selection (BAAS) technique was employed in this to minimize the number of iterations required for feature learning and antenna selection. In order to create an improved beamforming model for parallel antenna parameter estimation, this was processed through the hybridization of multi-objective optimization. Using the combination of Beamforming with BAAS based antenna selection approach, which chooses the optimal feature attribute of the antenna power spectrum with Direction of Arrival (DOA), this can further accelerate performance. Metrics like the quantity of allocated spectrum, power factor, throughput, spectrum range of capacity, and other relevant factors can be used to validate the effectiveness of the suggested strategy. The results show that the proposed work achieved approximately 7.27% of Spectral efficiency (bps), Signal Power (dB) and Interference (dB). [ABSTRACT FROM AUTHOR]

ABSTRACT Large‐scale multiple input multiple output (MIMO), also known as massive MIMO, serves as a transformative technology that effectively addresses the surging need for data‐intensive applications in the realm of 5G mobile networks and beyond. With a multitude of antennas at its disposal, a large‐scale MIMO base station possesses the capability to concurrently improve by orders of magnitude system spectral and energy efficiencies. Nevertheless, hardware cost and power consumption arise as serious challenge. To circumvent this latter, we investigate the potential of multilabel convolutional neural network (ML‐CNN) to develop an antenna selection (AS) model that based on the available channel state information (CSI) at the transmitter, it dynamically selects the optimal subset of antennas in real‐time. We evaluate the model performance using real indoor channel measurements under three different antenna array configurations. The obtained results demonstrate that the proposed ML‐CNN approach performs similarly to the convex relaxation‐based method, a mathematical technique often used for AS problems that provides optimal solutions with high computation cost, with the advantage of significantly reduced computation time. Moreover, it exhibits strong resilience across various antenna array configurations. Additionally, assessing the ML‐CNN's performance in scenario with imperfect channel estimation confirms its robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each antenna varies depending on the location of the base station and each user, and the transmit power after the digital beamformer is allocated to the antenna with the smallest gain. Signals transmitted from antennas with small gains are susceptible to noise and interference. Therefore, this paper proposes an HBF structure in which only the antenna with the highest gain forms the beam for each user. In the proposed scheme, the transmitting antennas are grouped and the beam is formed only by the group of antennas with the highest gain for each user. Simulation results show that the proposed scheme can reduce the number of phase shifters used on the transmit side compared to the conventional HBF scheme while maintaining sum-rate performance when the number of transmit antennas and users are the same. It was also shown that there is a trade-off between the reduction in the number of phase shifters used to form the beam and the improvement in performance as the number of transmit antennas increases. Furthermore, it is shown that when antenna selection is used, although there is a trade-off between the number of phase shifters and performance improvement, the number of phase shifters can be reduced while maintaining performance even when the number of transmit antennas increases. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) has emerged as a pivotal technology to address the escalating communication demands of Internet of Things (IoT). To meet the data transmission needs in IoT systems, we propose an antenna selection method of massive MIMO systems and joint power allocation strategy considering IoT user devices grounded in quantum energy valley optimization (QEVO) in this paper. The derivation of a maximum energy efficiency equation has been established to optimize system resources and provide high quality of service meeting the IoT user devices requirements. To tackle the nonlinear, multi-constrained hybrid optimization challenge proposed for massive MIMO resource allocation in IoT systems, we introduce a quantum energy valley optimization algorithm. This algorithm harnesses the strengths of quantum computation and energy valley optimization (EVO) mechanisms. Simulations indicate that our proposed method can efficiently meet real-time user transmission requirements while markedly enhancing system energy efficiency. When compared with existing power allocation strategies and optimization algorithms applied in massive MIMO communication systems, our approach demonstrates superior performance. The proposed method demonstrates the highest performance across various simulation scenarios when applied to both allocation strategies and system energy efficiency. Our proposed method with highest performance can be properly used on massive IoT devices. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) enables the concurrent transmission of numerous user signals, ameliorating spectral efficiency by superimposing these signals. NOMA users utilize energy harvesting to efficiently utilize available energy sources. The energy harvesting process is non-linear in general. Additionally, innumerable antennas are used at the NOMA transmitter and power source to facilitate efficient energy transfer and information transmission with antenna selection (AS). Also, wireless channels cause path loss, fading, and shadowing effects, which directly influence harvested energy and communication reliability. Accordingly, the work assesses the outage performance and throughput of NOMA, examining realistic aspects like non-linear energy harvesting (nlEH), AS, multiple antennas, and κ - μ shadowed fading. The findings indicate a considerable performance degradation due to nlEH. Moreover, appropriate parameter selection is crucial for preventing complete outages in NOMA networks and achieving optimal performance. In addition, the performance of NOMA networks meliorates with an accreting number of antennas. Furthermore, the AS criterion dramatically impacts performance difference between NOMA users. [ABSTRACT FROM AUTHOR]

One of the most important challenges in the fifth generation (5 G) of telecommunication systems is the efficiency of energy and spectrum. Massive multiple-input multiple-output (MIMO) systems have been proposed by researchers to resolve existing challenges. In the proposed system model of this paper, there is a base station (BS) around which several users and an eavesdropper (EVA) are evenly distributed. The information transmitted between BS and users is disrupted by an EVA, which highlights the importance of secure transfer. This paper analyzes secure energy efficiency (EE) of a massive MIMO system, and its purpose is to maximize the secure EE of the system. Several scenarios are considered to evaluate achieving the desired goal. To maximize the secure EE, selecting optimal number of antennas and cell division methods are employed. Each of these two methods is applied in a system with the maximum ratio transmission (MRT) and the zero forcing (ZF) precodings, and then the problem is solved. Maximum transmission power and minimum secure rate for users insert limitations to the optimization problem. Channel state information (CSI) is generally imperfect for users in any method, while CSI of the EVA is considered perfect as the worst case. Four iterative algorithms are designed to provide numerical assessments. The first algorithm calculates the optimal power of users without utilizing existing methods, the second one is related to the cell division method, the third one is based on the strategy of selecting optimal number of antennas, and forth one is based on a hybrid strategy. [ABSTRACT FROM AUTHOR]

In this paper, we analyze impacts of time-varying wireless channel characteristics on average sum rate and outage probability of downlink non-orthogonal multiple access (NOMA) systems with multiple-input multiple output (MIMO) antennas, where we employ the transmit antenna selection and maximal ratio combining schemes in order to enhance the sum rate. Through the simulation assuming Rayleigh fading channels, we present that the average sum rate significantly changes depending on the correlation coefficients of time-varying channels, and the outage probability is rarely changed by this correlation coefficient. These results are dependent on the transmit antenna selection scheme using time-varying channels. In addition, we show that the outage performance can be improved at the cost of the average sum rate when both NOMA and orthogonal multiple access (OMA) schemes are used in a hybrid manner. The results of this study can be used in system design considering time-varying channel characteristics. [ABSTRACT FROM AUTHOR]

Sixth Generation (6G) transceivers are envisioned to feature massively large antenna arrays compared to its predecessor. This will result in even higher spectral efficiency (SE) and multiplexing gains. However, immense concerns remain about the energy efficiency (EE) of such transceivers. This work focuses on partially connected hybrid architectures, with the primary aim of enhancing the EE of the system. To achieve this objective, the study proposes a combined approach of joint antenna selection and precoding, which holds the potential to further optimize the system’s EE while maintaining a satisfactory SE performance levels. The proposed approach incorporates antenna selection based on a meta-heuristic cyclic binary particle swarm optimization algorithm along with successive interference cancellation-based precoding. The results indicate that the proposed solution, in terms of SE and EE, performs very close to the optimal exhaustive search algorithm. This study also investigates the trade-off between SE and EE in a low and high signal-to-noise ratio (SNR) regimes. The robustness of the proposed scheme is also demonstrated when the channel state information is imperfect. In conclusion, this work presents a lower complexity approach to enhance EE in 6G transceivers while maintaining SE performance and along with a reduction in power consumption.

Abstract 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.

In this paper, the downlink of the multicast based spatial modulation systems is investigated. Specifically, physical layer multicasting is introduced to increase the number of access users and to improve the communication rate of the spatial modulation system in which only single radio frequency chain is activated in each transmission. To minimize the bit error rate (BER) of the multicast based spatial modulation system, a joint optimizing algorithm of antenna selection and multicast precoding is proposed. Firstly, the joint optimization is transformed into a mixed-integer non-linear program based on single-stage reformulation. Then, a novel iterative algorithm based on the idea of branch and bound is proposed to obtain the quasi-optimal solution. Furthermore, in order to balance the performance and time complexity, a low-complexity deflation algorithm based on the successive convex approximation is proposed which can obtain a sub-optimal solution. Finally, numerical results are showed that the convergence of our proposed iterative algorithm is between 10 and 15 iterations and the signal-to-noise-ratio (SNR) of the iterative algorithm is 1-2dB lower than the exhaustive search based algorithm under the same BER accuracy conditions. [ABSTRACT FROM AUTHOR]

Multiple Input Multiple Output (MIMO) systems offer interesting advantages in terms of throughput and reliability thanks to the diversity and spatial multiplexing technique. However, the system complexity and cost, caused by the RF elements, are still an ongoing challenge. To remedy this, antenna selection technique arises as an alternative to reduce the requirement on RF chains by selecting a subset of the available transmit antennas. This will decrease the overall cost without a significant degradation of the performance. The exhaustive search method, considered as optimal, scans all possible antennas subsets to select the optimal one. It involves a high computational complexity even more with the increase in the number of the antennas. Hence, transferring this huge computation to offline system seems to be the only way to benefit from this technique. In this paper, we propose to analyse the performance of the deep neural network to construct a deep learning decision server to assist the MIMO system for making intelligent decision for antenna selection that maximize either the capacity or the energy efficiency. Extensive simulations show that the designed network can suitably resolve the antennas selection problem with high accuracy and robustness against imperfect channel estimation while leading to low online computational time compared to different optimization-driven decision making method. [ABSTRACT FROM AUTHOR]

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This study proposes two approaches for improving the effectiveness of spatial modulation integrated into layer division multiplexing (SM-LDM) in broadcasting systems: biased-power allocation (Bi-PA) and shared antenna selection (SAS). Even though different data rates are employed in SM-LDM systems, Bi-PA enhances bit error rate (BER) fairness across layers. The ideal power ratios are adaptively determined by balancing signal-to-interference plus noise ratios with a preference for the lower layer (LL) that involves a higher modulation order. SAS alleviates the complexity of successive interference cancellation and enhances spectral and energy efficiencies. Both the LL and upper layer (UL) share the antenna selection decision and transmit using a single antenna. The UL carries a space shift keying signal while the entire power is allocated for the LL. We analyze the spectral efficiency for the SAS-based SM-LDM system with finite alphabet inputs. Numerical results demonstrate the advantages of the proposed approaches. Compared to pre-assigned-PA (Pre-PA), Bi-PA shows nearly identical BERs for both layers and solves the error floor problem. The sharing property and common layer transmission of SAS-based SM-LDM yield a significant BER reduction relative to conventional SM-LDM. It provides gains ranging from 7 to 15 dB for LL at BER equal to 10−3, while UL performance ranges from slight gain to minor loss. Furthermore, both Bi-PA and SAS techniques enhance the achievable LL rate and sum-rate at low and intermediate signal-to-noise ratio values. They can achieve an improvement of up to two bits in LL rate and less than one bit in sum-rate at a signal-to-noise ratio of −0.5 dB. These findings show that both proposed techniques have a considerable impact on enhancing the fairness, BER performance, and feasible rates of SM-LDM systems, making them promise for broadcast system designs. [ABSTRACT FROM AUTHOR]

The extreme growth in smart mobile users and the demand for higher data rates have paved the way for developing 5G networks. Additionally, increased wireless data traffic implies more power consumption in network equipment. Mobile network service providers are concerned about the energy efficiency (EE) of cellular networks. In this era of advanced wireless communication, Massive multiple input multiple output (MMIMO) technology satisfies the demand for a hyper-connected society. To deal with the problem of increased hardware power consumption and cochannel interference in MMIMO, a quad methodology is proposed to evaluate the performance of a cellular network. This quad package comprises linear precoding (LP), power consumption reduction (PCR), User selection (US), and antenna selection (AS). The proposed quad methodology is used for enhancing the energy efficiency and capacity of a cellular network. Two LP techniques, zero forcing (ZF) and minimum mean squared error (MMSE) are compared to evaluate the EE of MMIMO with antenna selection. It is observed that EE for MMIMO with AS using ZF is three times greater than MMSE. MMIMO system with the suggested quad methodology provides 45% and 20% improvement in capacity and energy efficiency respectively over only the norm US. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The problem of arbitrarily selecting only some of the antennas for signal reception in the multiuser massive multiple-input multiple-output (MIMO) uplink is examined. A binary quadratic program (BQP) based on maximizing the sum of signal-to-interference and noise ratio (SINR) is formulated to fulfill this task. It can then be relaxed to be convex and solved efficiently. Simulation results show that using the proposed antenna selection together with the minimum mean-squared error (MMSE) signal detection, if the number of receive antennas is reduced from N to Na, its error-rate performance can be better than that of the system originally equipped with Nareceive antennas employing the same MMSE signal detection.

This paper makes several contributions to antenna selection techniques for massive multiple-input multiple-output (mMIMO) systems using artificial neural networks. First, binary cross entropy is adopted as the loss function for network training instead of the conventional cross entropy, which reduces the number of nodes in the output layer from $\binom {N_{R}}{N_{RS}}$ to $N_{R}$ , where $N_{R}$ and $N_{RS}$ are the number of candidate antennas and the number of selected antennas, respectively. In mMIMO systems, which have a large number of antennas, binary cross entropy is essential. We also demonstrate that the channel matrix is practically sufficient information to train the network, excluding the signal-to-noise ratio (SNR) factor present in the capacity formula. Since a single label is generated for a given mMIMO channel regardless of SNR, the size of training data is reduced significantly. When the channel matrix without pre-processing is inputted into a neural network for feature extraction, which is referred to as pure connectionist feature extraction, we show that the convolutional neural network (CNN) extracts features more successfully than the fully connected network (FCN). We also show that hybrid feature extraction, in which features are first extracted symbolically from the channel matrix and then connectionist features are extracted from the symbolic features, offers significant performance improvement over pure connectionist feature extraction from the raw data. However, when features are extracted in a hybrid manner, FCN achieves marginally better performance than CNN, contrary to the pure connectionist feature extraction. Finally, when the networks in the hybrid feature extraction are pruned to be suitable for deployment in mobile devices, we show that FCN is a better choice, as it is more robust to severe pruning than CNN. We conducted computer simulations to demonstrate the effectiveness of the proposed approaches.

This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process.

Abstract As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the $$\ell _{2,1}$$ ℓ 2 , 1 -norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas.

The non-orthogonal multiple access (NOMA) technique developed for 5G next generation communication technology which has some promises such as providing high spectral efficiency has been the key point of researches lately. A new structure has been obtained by integrating the heterogeneous NOMA technique with the multiple input multiple output (MIMO) technique, which provides high data rate. Providing transmission with more than one antenna in this structure causes problems such as high cost, system complexity and excessive power consumption. Antenna selection has been the subject of this article in order to eliminate or minimize the problem. As a result of the literature studies on this subject, it has been noticed that the maxmax-max and max-min-max antenna selection algorithms are frequently used. However, only the speed performances of the algorithms were examined and other parameters were not evaluated in the studies. Here BER, outage probability, capacity analyzes of algorithms are discussed and simulation studies are carried out using MATLAB/Simulink program. From the results obtained, it was concluded that the BER values of the user who is close to the base station are better than the user who is far away, and as the distance to the base station decreases, the BER values improve, the capacity increases and the outage probability decreases. [ABSTRACT FROM AUTHOR]

Summary: One of the main imperfections degrading the performance of full‐duplex (FD) relaying systems is the outage floor. In this work, a power scaling method is proposed, which overcomes this effect even when there does not exist a direct channel between source and destination nodes. The system is composed of K decode‐and‐forward (DF) FD relays over the Nakagami‐m fading environment. To promote system performance, joint antenna and relay selection methods are employed in the FD relaying systems. Each FD relay is equipped with multiple antennas for receiving and the other for transmitting the information. The transmitting and receiving antennas are chosen based on the instantaneous channel variations, and one relay is selected to improve the signal‐to‐interference and noise ratio (SINR) of the FD relaying system. The performance of the proposed design is investigated. Moreover, the closed‐form equations of the ergodic capacity and outage probability are attained. The analytical results are confirmed by different simulations. Results indicate that the proposed design achieves an additional spatial diversity gain because of using the antenna selection at the relay nodes. Moreover, by power scaling (PS) method, the system performance is effectively improved compared to the conventional FD relaying structures. [ABSTRACT FROM AUTHOR]

In this study, under the Peak-to-Average Power Ratio (PAPR), energy, and binary (for antenna position selection) constraints, we proposed an antenna position selection and beam scanning method for colocated Multiple-Input Multiple-Output (MIMO) radar system using the min-max beampattern amplitude matching criterion. In our design, antenna positions and a set of probing waveforms were jointly determined to match a set of beampattern masks, and hence realize the beam scan. The resultant problem was large-scale, nonconvex, nonsmooth, and typical nondeterministic hard, because of the PAPR and nonconvex binary constraints, and the max and modulus operations in the objective function. To address these issues, we first transformed the min-max problem into the Iterative weighted Least Squares (ILS) problem using the Lawson algorithm, replaced the nonsmooth nonconvex objective function with the convex majorization function, and finally applied the alternating direction method of multipliers to solve the majorized ILS problem. Finally, several numerical examples were given to show the effectiveness of the proposed algorithms.

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

Summary: Massive multiple input multiple output (M‐MIMO) methods make reference to a useful method for using multipath propagation to communicate and receive multiple data signals at once over a single radio channel. To simultaneously transfer numerous data streams, it makes use of various antennas. The quantity of power used grows as the quantity of antennas rises. As a result, choosing the best transmit antennas, which is a major difficulty in M‐MIMO systems, becomes important. In this research, "Hybrid Sea Lion‐Whale Algorithm (HS‐WA)" is introduced by choosing a best transmit antenna while taking into account several objectives. This method optimizes overall capacity and efficiency. The chosen method combines the "Whale Optimization Algorithm (WOA) and Sea Lion Optimization Algorithm (SLnO)" that determines which antenna should be chosen while also optimizing the antenna quantity. Finally, energy efficiency (EE) and capacity analysis results demonstrate that the provided approach is superior to all other models. [ABSTRACT FROM AUTHOR]

This paper considers a cooperative wireless system with multiple antennas at the source. A multi-antenna relay equipped with a power splitter is introduced between the source and destination. It utilizes the energy from the received signal for amplification and transmit it to the destination. The linear precoding techniques such as zero forcing (ZF) and minimum mean square error (MMSE) are used to reduce the interference. We compare two antenna selection techniques viz. norm-based antenna selection (NBAS) and received signal-to-noise ratio (SNR) based antenna selection (RSBAS). It is analyzed that the NBAS and RSBAS antenna selection techniques offer improvement of 20% and 37% over random antenna selection technique. However, the antenna selection technique NBAS offers improvement in system bit error rate (BER) performance by 14% as compared to RSBAS at SNR value of 0 dB. We presented a scenario where the energy harvesting at relay node offers saving the battery power at the user node. Hence, the presented analysis is applicable in communication with remote sensors. This paper also analyses that the BER is improved with the reduction in the distance between source and relay for the given value of energy harvesting efficiency. [ABSTRACT FROM AUTHOR]

As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the ℓ 2 , 1 -norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas. [ABSTRACT FROM AUTHOR]

Summary: In the realm of 5G mobile wireless communication, cognitive radio (CR) plays an important role in improving radio spectrum efficiency by overcoming challenges such as spectrum shortage versus under‐utilization associated with the traditional fixed frequency assignment. "Interweave CR, underlay CR, and overlay CR" are the three primary paradigmatics in general. The present study focuses on the overlay CR paradigm, wherein secondary users (SUs) have all been expected to be provided with superior "encoding and signal processing techniques" to improve primary users' (PUs') communication. Furthermore, to address concerns associated with inefficient spectrum usage, multiplexing methods like "non‐orthogonal multiple access (NOMA) and spatial modulation (SM)" have been implemented. NOMA makes use of the power domain to maximize spectrum usage, whereas SM makes use of the spatial domain. Multiple antennas, on the other hand, are expensive in terms of energy, magnitude, and equipment. Antenna selection is a low‐cost, low‐complexity method of capturing many of the NOMA system's advantages. As a consequence, this study offers the fitness familiarized lion algorithm (FFLA), a novel methodology for selecting the best antennas. Furthermore, the selection procedure involves identifying the stated objectives, which include energy usage and error minimization. Indeed, the suggested technique is an enhanced version of the lion algorithm (LA). Ultimately, the suggested work's performance is compared to and proven against other traditional models. [ABSTRACT FROM AUTHOR]

For future wireless communication, millimeter wave (mmWave) coupled with the massive multiple-input multiple-output (MIMO) are key technologies to overcome the huge data rate requirements. Although massive MIMO greatly improves the spectral efficiency (SE) of the system, the use of large antenna arrays not only increases the computational complexity it may also decrease the energy efficiency. Focusing on improvement in energy efficiency, we propose a low-complexity solution for joint transmit antenna selection and hybrid precoder design for multi-user mmWave Massive MIMO communication systems. Particularly, considering a partially connected hybrid architecture, binary particle swarm optimization and deep neural network (DNN) algorithms are employed for transmit antenna selection and analog precoder design, respectively. Results show that the proposed solution performs very close, in terms of spectral efficiency, to the optimal exhaustive search based antenna selection and singular value decomposition based precoder design with lower computational complexity. It is also shown that the proposed solution also improves the energy efficiency of the system. Finally, the proposed solution is not very sensitive to channel imperfections.

This paper studies the performance of a 14-element dual-band 5G MIMO antenna with body effect and an antenna selection. The antenna operates in the lower frequency band from 3.10 to 3.85 GHz for 5G bands of LTE 42 and 43, whereas the upper band is from 5.60 to 7.20 GHz for the newly assigned 6 GHz WiFi band. The antenna performance was evaluated in free space and under the effect of human body in three scenarios, namely one-hand, two-hands, and call mode. All resulting envelope correlation coefficients are less than 0.3. On the other hand, the free space efficiency of the antenna elements is between 41 % to 77 % in the lower band and 61 % to 95 % in the upper band. Meanwhile, the efficiency of the elements varies depending on the interaction between each element with the hands and the head, with the least obstructed element efficiency degrading to 8 % relative to free space levels. Due to the body effect, multiplexing efficiency loss varies from 2.68 dB to 5.93 dB and the capacity loss is from 14 % to 38 %. Finally, a selection algorithm is used to assess the performance of the overall antenna when 12, 10 and 8 antenna elements are selected for operation. In free space and with number of elements 12, 10 and 8, the system achieves 91.7 %, 80 % and 67 % from the capacity of the 14-element MIMO antenna, respectively. However, in the vicinity of the body, these values increase to 96.3 %, 85.0 % and 72 % from the capacity of the 14-element MIMO antenna, respectively. This signifies that the less-contributing elements as a result of body blockage can be deselected in order to preserve system resources that these elements consume while contributing less significantly to the performance.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Antenna selection (AS) and intelligent reflecting surface (IRS) are two promising technologies to reduce the hardware cost of multi-antenna systems. This letter intends to jointly design the users’ active transmit beamformers, the IRS’s passive beamformer, and the base station’s receive AS network in order to maximize the uplink sum rate of an IRS-aided multiuser multiple-input multiple-output (MU-MIMO) system. An efficient algorithm is proposed to solve the resultant non-convex problem via capitalizing on the block coordinate descent and fractional programming techniques. Numerical results show that compared with other baseline methods, the proposed algorithm can significantly improve the uplink sum rate. [ABSTRACT FROM AUTHOR]

Summary: Incorporation of multiple antennas in downlink non‐orthogonal multiple access (NOMA) system helps in achieving better performance by exploiting different diversity schemes. To this end, we propose a general framework of transmit antenna selection (TAS)‐assisted orthogonal space‐time block code (OSTBC) transmission for multiple NOMA users. We derive exact expressions of probability density function of the TAS‐OSTBC processed signal‐to‐noise ratio (SNR) at the output of maximum‐ratio combiner of the NOMA users. Using well‐known moment generating function approach, we evaluate the error performance of TAS‐OSTBC assisted NOMA users over generalized η−μ fading channel in presence of perfect and imperfect successive interference cancelation. We investigate the proposed system in different multi‐antenna scenarios and accordingly observe better error performance of the users by increasing the number of transmit and receive antennas. We perform their asymptotic analysis leading to full‐diversity order at high SNR. For numerical evaluation, we truncate the infinite series in derived results and examine the upper bound of truncation error. We evaluate nth moment of the output SNR and obtain channel quality estimation index as a performance measure of the proposed NOMA system. Moreover, we investigate the influence of power coefficients and fading parameters, η and μ, on the error performance of TAS‐OSTBC‐assisted NOMA users. We also compare the derived error performances with the existing schemes, wherein we observe that the proposed system shows remarkable improvement in the bit error rate (BER) of the NOMA users. For validation, we execute simulations which closely match with the derived analytical results. [ABSTRACT FROM AUTHOR]

As an efficient and interference-resistant coding technique, multiple description coding (MDC) is proposed with the aim to solve the transmission unreliability problem caused by packet errors, loss or blocking delays. The integration of MDC with non-orthogonal multiple access (NOMA) scheme (MDC-NOMA) can effectively boost the robustness and throughput of the underlying system. Additionally, space-time block coding (STBC) can further enhance the system reliability with increased diversity gain, as well as reduced decoding complexity. In this paper, we propose a novel framework referred to as MDC-NOMA-STBC, in which we apply Alamouti STBC integrated with MDC and NOMA to implement more reliable transmission. Specifically, NOMA signals are constructed by superimposing the descriptions from different users, then transmitted by the base station (BS) using Alamouti STBC. In order to substantiate the performance of the proposed framework, first, we derive closed-form expressions of both the outage probability and ergodic rate for each user in a two-antenna BS scenario. Second, for multi-antenna BS, we investigate different antenna selection strategies to further enhance the outage performance under the considered framework, whose analytical expression is provided wherever possible. Third, Monte Carlo simulation results are presented to validate our theoretical framework and to corroborate the superiority of the proposed MDC-NOMA-STBC over state-of-the-art MDC-NOMA scheme. Moreover, under realistic contexts with image transmission, it is confirmed that MDC-NOMA-STBC outperforms its counterpart MDC-NOMA in terms of the peak signal-to-noise ratio and the bit error rate. [ABSTRACT FROM AUTHOR]

Underlay spectrum sharing addresses spectrum scarcity but imposes constraints on the interference the secondary system causes to the primary receiver. For a secondary transmitter that is subject to the stochastic and general interference-outage constraint and the peak transmit power constraint, we present a novel joint antenna selection and power adaptation rule. It addresses the twin goals of improving the spectral efficiency and reducing the power consumed of an underlay secondary system with low hardware complexity and cost. We prove that the rule maximizes the energy-efficiency (EE) of the secondary system. Its form differs from all other rules considered in the literature. We then present an insightful geometrical characterization of the optimal power of the selected antenna in terms of the channel gains within the secondary system and between the secondary and primary systems. Our approach leads to several special cases, which are themselves novel, and novel analytical insights about the performance and structure of the optimal rule. We also present an iterative subgradient-based algorithm and a simpler non-iterative bound-based algorithm to compute the rule’s parameters. The rule achieves a markedly higher EE compared to conventional approaches, and serves as a new fundamental benchmark for antenna selection in underlay spectrum sharing. [ABSTRACT FROM AUTHOR]

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Importance of the signal reliability has started to increase in today’s communication technologies. One of the most important methods of increasing signal reliability for communication systems is the selection of antennas at the receiver, transmitter, or both. On the other hand, having low complexity, low processing times and high performance, deep learning (DL) based technologies have started to find a place in various fields such as communication, warehouse management, health services, image processing, and so on. In this letter, we first introduce the joint transmit and receive antenna selection (JTRAS) for spatial modulation (SM) systems, which, as far as we know, has not been studied in detail before, and second, we perform the DL algorithms for JTRAS instead of its exhaustive search. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In this paper, with the aim of performing joint multi-target localization and discrimination tasks, a performance-driven resource allocation scheme is proposed. In the first, by establishing the signal model under deception jamming and utilizing the maximum likelihood (ML) estimator, the estimation information of targets can be obtained. Secondly, the Cramer–Rao lower bound (CRLB) for the transmit antenna selection and power allocation is derived. Then, to fully utilize the difference in spatial distribution between true and false targets, a false target discriminator based on the CRLB of the distance deception parameter is utilized. By introducing the nondimensionalization mechanism, we build an optimal objective function of target localization error and discrimination probability. Subsequently, a joint multi-target localization and discrimination optimization model has been established, which is mathematically a non-smooth and non-convex problem. By introducing an auxiliary variable, we propose a three-step solution strategy for solving this problem. Simulation results demonstrate that the proposed algorithm can improve the performance of joint localization accuracy and discrimination ability (JLADA) by more than 30% compared with the algorithms only for localization or discrimination. Meanwhile, by utilizing the proposed algorithm, the composite indicators of JLADA can decrease more than 70% compared with the uniform allocation scheme. [ABSTRACT FROM AUTHOR]

We elucidate feasibility of wireless energy transfer (WET) with the help of an intelligent reflecting surface (IRS). We consider a source equipped with multiple antennas and a single radio-frequency (RF) chain. We propose a low complexity rule that does joint antenna selection (AS) at source and passive beamforming at IRS. We derive new expressions for probability of outage in WET under perfect and estimated channel knowledge and for both single and multiple users. We derive intuitive expressions for outage probability with large number of IRS elements and for line-of-sight scenarios. For a system with $M$ antennas at source and $N$ passive elements at IRS, we show that diversity order equals $M+N$. Extensions to subset AS, discrete phase-shift design, and performance under limited scattering are also presented. Our numerical results show that the proposed AS rule yields near-optimal performance while requiring only $M+N$ pilot transmissions compared to the $M+MN$ pilot transmissions required by the optimal AS rule in literature. We elucidate that we can trade-off active RF chains at source with passive elements at IRS to obtain improved performance both in terms of outage probability and power transfer efficiency. And 3-bit IRS is sufficient to obtain good performance at lower complexity. [ABSTRACT FROM AUTHOR]