Different Number Of Antennas Research Articles

Millimeter Wave Massive MIMO systems using Hybrid Beamforming

The goal of next-generation wireless systems is to support many users by achieving higher data rates and reduced latency. Multiple input and multiple output systems (MIMO) are utilized in order to achieve high data rates. Multiple antennas are employed by Massive MIMO systems in both the transmitter along with the receiver. A signal processing method known as beamforming is used on several transmitting and receiving stations in order to deliver and receive multiple messages at once. To increase spectral efficiency, hybrid beamforming using a uniform rectangular antenna array is used in this research work. The results of hybrid beamforming using different numbers of antennas are compared with those of fully digital beamforming. A comparison between signal-to-noise (SNR) and bit error rate (BER) is performed. Comparing hybrid beamforming to fully digital beamforming, simulation findings show that hybrid beamforming reduces computing complexity due to reduced number of RF links. Also observed that the spectral efficiency rises with an increase in the quantity of transmitting antennas.

Outage probability analysis for downlink NOMA ARS-assisted cooperative communication system with trajectory optimization

In this paper, we propose and investigate the performance of a downlink non-orthogonal multiple access multi-user system with the assistance of an aerial relay station (ARS). The ARS is equipped with multiple antennas, mounted on an unmanned aerial vehicle (UAV), and serves as a relay for cooperative communication between a terrestrial base station (BS) and users. The system performance is evaluated via the outage probability of each user in the urban environment where the ARS and BS are configured with a different number of antennas. More interestingly, the optimization of the ARS trajectory to maximize the data rate of the considered system is also proposed by using the successive convex approximation technique. The results show that the best system performance can be obtained by adjusting the altitude and designing the optimum trajectory of the ARS. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed system and the accuracy of the developed analytical frameworks.

Gain of Spatial Diversity with Conjoint Signals in Arbitrarily Correlated Rayleigh Fading Channels

Coding gains for arbitrarily correlated signals in a spatial diversity system with conjoint signals are presented in this study. The basic form of the proposed signal synthesizer evenly produces phase changes in the output signals. The mixer is an orthogonal transformation matrix, which is energy preserving and blind to the channel correlation matrix. The idea is to synthesize additional conjoint signal copies from the received signals that would be received if there were more antennas. However, these conjoint signals contain a level of correlation with the received signals. With the assumption of flat Rayleigh fading channels, simulation results for symbol error probability (SEP) are presented for different numbers of receive branches and varying correlation conditions. It is shown that under binary phase shift keying (BPSK), the synthesizer achieves decorrelation coding gains of about 1 dB when selection combining (SC) or equal gain combining (EGC) is used. The synthesizer’s performance across M-ary quadrature amplitude modulation (MQAM) signals is also tested. In addition, analytical frameworks are derived for BPSK and MQAM, which are tightly bound by the Monte Carlo simulation results obtained using Matlab. The correlation analysis is performed for different numbers of antennas and varied antenna spacings.

Secrecy Performance Analysis for MIMO-DF Relay Systems With MRT/MRC and TZF/MRC Schemes

Transmit beamforming is a diversity technique in multiple-input multiple-output (MIMO) systems. It improves the diversity order and enhances the information security of the MIMO systems. In this paper, we mathematically analyze the secure performance of a MIMO decode-and-forward (DF) relay system under the presence of an eavesdropper and with the imperfect channel state information (CSI) of the eavesdropping channel. To enhance the secrecy performance, we employ multiple antennas at all legitimate nodes and utilize two linear beamforming schemes, i.e., maximal ratio transmission (MRT) and transmission zero-forcing (TZF), at the transmitter and maximal ratio combining (MRC) scheme at the receiver. The probability density function (PDF) of the output SNR for different numbers of antennas at the transmitter and receiver is plotted and discussed. After that, we apply this PDF to derive the closed-form expressions of the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) of the considered MIMO-DF relay system over Rayleigh fading channels for MRT/MRC and TZF/MRC schemes. In addition, based on the obtained analysis results, we provide the location of the relay that minimizes the system SOPs. All the analysis results are validated by Monte-Carlo simulations. The results show that the TZF/MRC scheme provides better SOP and ESC than the MRT/MRC scheme. Moreover, the number of antennas at the legitimate nodes and the location of the eavesdropper significantly impact the secrecy performance.

Direction of arrival estimation for BLE: Antenna array design and evaluation

The use of smart antenna arrays in wireless communication systems is becoming increasingly popular, as they improve the performance of the radio link and allow the integration of communication and sensing functions. In this paper, we present the design and performance evaluation of a uniform circular antenna array (UCAA) with 12 monopole antennas and RF switches to select the active antenna for the signal direction of arrival (DoA) estimation. The antenna configuration was optimized through simulations for a different number of antennas and antenna array radius. The frequency tuning of the antennas considered the coupling of the active antenna with adjacent antennas. The performance of the antenna array was evaluated by (1) simulations that examined the proximity of the antenna to the edge of the antenna array and the influence of adjacent antennas on the performance of the active antenna, and (2) measurements in outdoor and indoor environments. The results of the simulations show that antenna coupling can cause nulls in the radiation pattern, which affects the DoA estimation by reducing the signal-to-noise ratio. In a semi-controlled environment, we achieved a DoA estimation error of less than 1 degree by using the Bluetooth Low Energy (BLE) Angle of Arrival feature.

Physical Layer Anonymous Precoding Design: From the Perspective of Anonymity Entropy

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal.

MIMO system capacity based on different numbers of antennas

This paper analyzed and compared the ergodic channel capacities of several typical channel systems (SISO, SIMO, MISO, MIMO) in independent identically distributed flat Rayleigh fading channels. The theoretical and simulation results show that the ergodic capacity of the MIMO channel increases linearly with the number of antennas.

Beam Tracking for Distributed Millimeter-Wave Massive MIMO Systems Based on the Unscented Kalman Filter

In this letter, a beam tracking problem is studied in distributed millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) systems. Firstly, a monopulse beam direction estimation method for a distributed mm-wave massive MIMO system is introduced. However, this method is very sensitive to noise and must be performed in every time slot in a single-user moving scene. To improve its accuracy, a monopulse beam tracking method based on the unscented Kalman filter (UKF) for a distributed mm-wave massive MIMO system is proposed. The simulation results show that the proposed beam tracking method has better tracking performance under different SNRs and different numbers of antennas of remote antenna units (RAUs), and its robustness and generalizability are quite well.

User-Heterogeneous Cell-Free Massive MIMO Downlink and Uplink Beamforming via Tensor Decomposition

We consider a cell-free massive MIMO (CF-mMIMO) system in which multiple access points (APs), connected to a common central processing unit (CPU) through unbounded fronthaul, collaboratively serve multiple users in a heterogeneous scenario in which each user equipment (UE) has a different number of antennas, and therefore is capable of communicating via distinct numbers of digital streams. For such a user-heterogeneous system, new joint transmit (TX)/receive (RX) beamforming (BF) algorithms are then proposed, both for downlink and uplink modes and integrated with two alternative transmit (TX) power and spatial resource allocation strategies, which enable interference-free communications. To that end, a novel tensor decomposition scheme is presented, based on an orthogonality-enforcing modification of the recently-proposed multilinear generalized singular value decomposition (ML-GSVD). Simulation results show both that the new orthogonality-enforcing ML-GSVD (OEML-GSVD) achieves greater accuracy than the previous multilinear generalized singular value decomposition (ML-GSVD) without sacrificing convergence speed, and that the corresponding OEML-GSVD-based proposed beamformers outperform state-of-the-art (SotA) techniques, as well as an equivalent beamformer based on the previous ML-GSVD alternative.

SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas

MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, the joint processing of signals for transmission and reception differs this radar from the multistatic radar. There are many resource optimization problems which improve the performance of MIMO radar. But power allocation is one of the most interesting resource optimization problems. The power allocation finds an optimum strategy to assign power to transmit antennas with the aim of minimizing the target tracking errors under specified transmit power constraints. In this study, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. Therefore, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is calculated. This is utilized as a power allocation problem objective function. Since the proposed power allocation problem is nonconvex. Therefore, a SQP-based power allocation algorithm is proposed to solve it. In simulation results, the performance of the proposed algorithm in various conditions such as a different number of antennas and antenna geometry configurations is examined. Results affirm the accuracy of the proposed algorithm.

An effective PSO-based power allocation for target tracking in MIMO radar with widely separated antennas

In MIMO radar with widely separated antennas, the antennas are spaced far from each other and the target is seen from different angles. In this type of radars, each receiver collects all transmit signals and transmits them to the central processor unit. Power allocation is an important part of military operations. Therefore, it is a primary factor that requires to be taken into account in the designing of target tracking problems in MIMO radar. In fact, the power allocation finds an optimum strategy to allot power to transmit antennas with the goal of minimizing the target tracking errors under specified transmit power constraints. In this paper, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. For this purpose, first, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is computed. This is applied as a power allocation problem objective function. Because a complex Gaussian model is considered for target radar cross-section (RCS), this function becomes complicated. Due to the nonlinearity of this objective function, the proposed power allocation problem is nonconvex. Therefore, a particle swarm optimization (PSO) -based power allocation algorithm is proposed to solve it. In simulation experiments, the performance of the proposed algorithm in different conditions such as a different number of antennas and antenna geometry configurations is evaluated. Results prove the validity of the proposed algorithm.

Physical Layer Security Enhancement Using Artificial Noise in Cellular Vehicle-to-Everything (C-V2X) Networks

The secure transmission of confidential information in cellular vehicle-to-everything (C-V2X) communication networks is vitally important for user's personal safety. However, for C-V2X there have not been much studies on the physical layer security (PLS). Since artificial noise (AN) and secure beamforming are popular PLS techniques for cellular communications, in this paper we investigate the potential of these PLS techniques for enhancing the security of C-V2X networks. In particular, leveraging stochastic geometry, we study the PLS of an AN assisted C-V2X network, where the locations of legitimate vehicular nodes, malicious vehicular nodes and road side units (RSUs) are modeled by Cox processes driven by a common Poisson line process (PLP), and the locations of cellular base stations (BSs) are modeled by a two-dimensional (2D) Poisson point process (PPP). Based on the maximum signal-to-interference-ratio (SIR) association scheme, we calculate the coverage probability of the network. We also derive bounds on the secrecy probability, which are validated by simulation results. Moreover, we obtain an analytical result of the effective secrecy throughput for characterizing the reliability and security of wiretap channels. Simulation results are given to validate the analytical result, and provide interesting insights into the impact of network parameters on the achievable secrecy performance. Simulation results show that a larger array antenna can provide a better robustness of the secure transmission strategy, and the optimal power allocation ratio between confidential information and AN remains almost unchanged for different numbers of antennas.

Mobile Image Communications Using Deep Neural Networks for Heterogeneous User Devices in MIMO Channels

This article considers a mobile multimedia communication scenario in which two types of heterogeneous users with different numbers of antennas and different display resolutions are served within the same service area. We propose an efficient image transmission scheme that makes use of deep neural network-based super-resolution techniques, spatial diversity, and diversity-multiplexing tradeoff (DMT) achieving codes. The proposed scheme transmits a low-resolution (LR) image to two types of users, along with residual pixel-error map that contains high-frequency details of high-resolution (HR) images and is produced by a convex optimization technique. Then, a user retaining an HR screen employs super-resolution to reconstruct an HR image from the received LR image, and also exploits the residual map to further enhance the image quality. Our scheme properly trains deep neural network models for super-resolution, based on the source coding rates of the images to be transmitted over bandwidth-limited multiple-input multiple-output (MIMO) channels. That is, various quantization errors resulting from image compression are considered when training the deep neural network-based super-resolution models. For MIMO systems, considering and utilizing the relationship between the number of antennas and the screen resolution, which is based on hardware space of user devices, the proposed scheme encodes an LR image with spatial diversity techniques, and encodes a residual pixel-error map with DMT-achieving codes. Numerical evaluation shows that our scheme significantly outperforms the strategy that broadcasts either HR or LR images to two types of heterogeneous users.

An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 2 × 2 and 2 × 1 and space-time transmit diversity with a 4 × 4 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.

Unified OSTBC‐based communication scheme for cooperative space vehicles

In many spatial cooperative vehicle networks, omni-directional communication is preferred due to its flexibility for networking. In the recent studies, space time code was shown to be an effective approach for improving the performance of the omni-directional communication between space vehicles. This paper studies the unified scheme for the orthogonal space time block code–based transmission between two space vehicles equipped with different numbers of antennas. It is obtained that at most six passive antennas and four RF chains are enough for the typical use. In the proposed scheme, the receiver can perform a unified detection procedure regardless of the actual number of the transmit antennas, which facilitates the practical application. Simulation results validate the proposed scheme in terms of the cumulative distribution function of the received signal-to-noise ratios.

Interference Coordination for Heterogeneous Users in Asynchronous Fog Radio Access Networks

In this letter, we propose a novel multi-input multi-output (MIMO) interference coordination (IC) technique for uplink fog radio access networks (F-RANs), consisting of two multi-antenna remote radio heads (RRHs) connected to a single fog access point (F-AP) and user equipments (UEs) equipped with different numbers of antennas. It is assumed that UEs are scheduled with an asynchronous manner for each RRH. With the proposed MIMO IC technique, the interference dimension at each RRH is dynamically adjusted via transmit beamforming at the selected UEs based on both currently scheduled UEs and their antenna configurations. To the best of our knowledge, the MIMO IC exploiting heterogeneous multi-antennas at UEs is first proposed in this letter for the F-RANs. Numerical results show that the proposed technique outperforms the conventional schemes in terms of sum rates.

Joint Source-Relay Optimization for MIMO Full-Duplex Bidirectional Wireless Sensor Networks with SWIPT.

The simultaneous wireless information and power transfer (SWIPT) technique has been considered as a promising approach to prolong the lifetime of energy-constraint wireless sensor networks (WSNs). In this paper, a multiple-input multiple-output (MIMO) full-duplex (FD) bidirectional wireless sensor network (BWSN) with SWIPT is investigated. Based on minimum total mean-square-error (total-MSE) criterion, a joint optimization problem for source and relay beamforming and source receiving subject to transmitting power and harvesting energy constraints is established. Since this problem is non-convex, an iterative algorithm based on feasible point pursuit-successive convex approximation (FPP-SCA) is derived to obtain a local optimum. Moreover, considering the scenarios in which source and relay nodes equipped with the same and different numbers of antennas, a low-complexity diagonalizing design-based scheme is employed to simplify each non-convex subproblem into convex problems and to reduce the computational complexity. Numerical results of the total-MSE and bit error rate (BER) are implemented to demonstrate the performance of the two different schemes.

Optimum Sparse Subarray Design for Multitask Receivers

The problem of optimum sparse array configuration to maximize the beamformer output signal-to-interference plus noise ratio (MaxSINR) in the presence of multiple sources of interest (SOI) has been recently addressed in the literature. In this paper, we consider a shared aperture system where optimum sparse subarrays are allocated to individual SOIs and collectively span the entire full array receiver aperture. Each subarray may have its own antenna type and can comprise a different number of antennas. The optimum joint sparse subarray design for shared aperture based on maximizing the sum of the subarray beamformer SINRs is considered with and without SINR threshold constraints. We utilize Taylor series approximation and sequential convex programming techniques to render the initial nonconvex optimization a convex problem. The simulation results validate the shared aperture design solutions for MaxSINR for both cases where the number of sparse subarray antennas is predefined or left to constitute an optimization variable.

Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

Carrier frequency offset (CFO), which often occurs due to the mismatch between the local oscillators in transmitter and receiver, limits the performance of multiple-input multiple-output (MIMO) wireless communication systems. To recover the CFO, the first step is coarse CFO estimation. This paper presents a neural network (NN) based coarse CFO estimator which has higher compatibility with a variety of MIMO systems, comparing with traditional CFO estimators. Instead of performing closed form calculation as some traditional estimators do, the proposed estimator transforms the estimation problem to a classification problem: classify the optimal coarse CFO estimate from a pool of coarse CFO candidates. Taking the advantage of neural networks, the proposed NN estimator can perform coarse CFO estimations for MIMO systems with different numbers of antennas and a variety of channel models. Meanwhile, the testing results show that the proposed NN estimator has promising performance and wide CFO acquisition range.

COMPARISON BETWEEN MLD AND ZF ALGORITHMS FOR MIMO WIRELESS SYSTEM AT RAYLEIGH CHANNEL

In this paper, the performance of the Multiple Input Multiple Output (MIMO) technique evaluated in term of Bit Error Rate (BER) with respect to Signal to Noise Ratio (SNR) by using Binary Phase Shift Keying (BPSK) modulation for two algorithms Maximum Likelihood (MLD) and Zero-Forcing (ZF) with different configurations of antennas array in Rayleigh and Additive White Gaussian Noise (AWGN) channels. The results were compared between them to determine which of the numbers antenna elements are suitable in the transmitter and receiver of each algorithm. The results of MLD offers a better configuration when 4×4 and 3×4 antennas array were used, while the ZF remains the same performance for the 2×2, 3×3 and 4×4 configurations. In different numbers of antennas, the best performance of ZF is got when the number of transmitter and receiver antennas are equal to 2×4 respectively. Also, the last one is better than the 4×4 and 3×4 configurations of MLD algorithm.