MISO Systems Research Articles

Characterization of Transparent Solar Cells

This article presents the results of the characterization of a transparent photovoltaic cell, which is part of one of the most recent technologies in photovoltaic system developments. The study emphasizes the parameters that best determine the performance of the Transparent Solar Cell (TSC), with the purpose of supporting future research aimed at understanding its performance. Additionally, a mathematical model representing the electrical dynamics of the system is proposed. The model is obtained through identification, a technique based on the treatment of experimental data. Its formulation is achieved using a Hammerstein-Wiener mathematical structure for non-linear MISO systems.

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Performance analysis of IRS-assist wireless communication system with Alamouti transmit diversity scheme

Intelligent Reflecting Surface (IRS) is an emerging and growing technology for upcoming B5G and 6G wireless standards to design a perfect smart radio environment. In this article, we try to enhance the physical layer performance of the IRS- assist wireless communication system by taking some helpful performance metrics such as the outage probability (OP), average bit error rate (ABER), and average capacity of the system. Here, we assume our system is mainly affected by phase shift noise and quantization noise due to a discrete number of phase shifts of the IRS elements. The Alamouti STBC transmitter diversity technique is used to design the IRS-assist MISO wireless communication system to mitigate the effect of these kinds of noise, leading to the works’ novelty. We have derived a new and most accurate mathematical framework of PDF and MGF of the corresponding proposed IRS-assist wireless communication system with an end-to-end signal-to-noise ratio (SNR). Behind this statistical distribution, we expressed unique and more precise closed-form expressions of each performance metric under IRS assist SISO and MISO wireless link for the entire SNR region and it is significantly observed that the proposed MISO system provides better outcomes than the general SISO system. Furthermore, we calculate the diversity order of both configurations and it shows that the diversity order of the links is associated with the number of reflecting elements, phase noise error, and average magnitudes of the system. We also validated our analytical outcomes using Monte Carlo simulation results from MATLAB.

Two-Timescale-Based Beam Training for RIS-Aided Millimeter-Wave Multi-User MISO Systems

Reconfigurable intelligent surfaces (RISs), as a promising technology for 6 G communications, have received considerable attention. Herein, we investigate the downlink precoding in the RIS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) system, where the access point (AP) uses hybrid digital and analog precoders. Due to the constraints of practical hardware and the difficulty of acquiring channel state information, we consider codebook-based passive beamforming and analog beamforming, and then formulate the downlink precoding problem as a mixed-integer nonlinear programming (MINLP) problem. To solve this NP-hard MINLP problem with low channel estimation overheads, we investigate beam training for the codebook-based passive beamforming and analog beamforming, and the MINLP problem is then reduced to a traditional MU-MISO precoding problem. To further reduce the beam training overheads, we propose a two-timescale-based beam training (TT-BT) algorithm. We prove that the TT-BT algorithm achieves the maximum codebook-based passive and analog beamforming gain. To avoid the limitations imposed the use of the TT property, we design the analog precoder by aligning the analog beamforming to RISs. Moreover, we characterize the rate loss due to codebook-based quantization. Compared with the empirical BT (EBT) algorithm, the proposed TT-BT algorithm achieves better performance with significantly lower training and feedback overheads.

Fairness-based transmission design in intelligent reflecting surface aided multi-user MISO systems

This paper investigates fairness-based transmission design in Intelligent Reflecting Surface (IRS) assisted downlink transmission scheme for multi-user MISO system. IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the interference among users. The minimum weighted signal-to-interference-plus-noise ratio (SINR) of users is maximized by jointly optimizing the precoding matrix and the phase shift matrix of IRS in order to improve fairness among users. We proposed Successive Convex Approximation-Alternating Optimization (SCA-AO) method to solve the non-convex problem, and analyze the effect of weighted factors on performance. Verified by simulation results the approach of SCA-AO performance is better then method of Exact-Alternating-Optimization (EAO).

A modified deep learning based MIMO communication for integrated sensing, communication and computing systems

The future wireless networks are expected to be data-driven integrated sensing, communication and computing (ISCC) systems and benefit from employing deep learning to process the large amount of data with less resources and computational limit. As a result, deep learning based learning MIMO communication has been widely discussed in recent years and show fascinating performance advantages over human designed MIMO systems. However, we find that the autoencoder frameworks proposed in current deep learning based methods are not able to be implemented under multipath channels, which is the most common channel model in actual environment. To overcome this problem, a modified autoencoder framework is proposed in this paper. This proposed autoencoder framework introduces well designed convolutional neural layers into the widely used feedforward neural networks, and demonstrates significant performance improvements over traditional Turbo code method adopted in existing wireless MIMO systems. Moreover, computer simulations are performed and the proposed autoencoder framework is also demonstrated to be effective in SISO and MISO systems under AWGN and Rayleigh fading channels, which verifies the generality of the proposed autoencoder framework for end-to-end learning communication systems.

Discrete phase shifts control and beam selection in RIS-aided MISO system via deep reinforcement learning

Discrete phase shifts control and beam selection in RIS-aided MISO system via deep reinforcement learning

A Deep Reinforcement Learning Approach to Two-Timescale Transmission for RIS-Aided Multiuser MISO systems

Reconfigurable intelligent surface (RIS) has drawn great attention recently as a promising technology for future wireless networks. In this letter, considering the two-timescale transmission protocol, we investigate the joint design of the transmit beamforming at the base station (BS) with instantaneous channel state information (CSI) and the RIS phase shifts with statistical CSI. Due to the large number of RIS elements, this design issue usually suffers from high computational complexity. To resolve the non-convexity issue with low complexity, we propose a novel deep reinforcement learning (DRL) framework, which contains two agents applying proximal policy optimization (PPO) based algorithm. Experiment results demonstrate that the proposed algorithm has comparable spectral efficiency performance to the state-of-the-art methods with substantially reduced computational delay.

Channel-Aware Placement of Active and Passive Elements in Hybrid RIS-Assisted MISO Systems

Channel-Aware Placement of Active and Passive Elements in Hybrid RIS-Assisted MISO Systems

IRS-Assisted MISO System With Phase Noise: Channel Estimation and Power Scaling Laws

Recent studies have shown that intelligent reflecting surfaces (IRS) can significantly improve the spectral and energy efficiency of wireless communication links. However, most works assume perfect transceivers and IRS, which is impractical in real communication systems. In this work, we study the effect of the hardware impairments in IRS-assisted MISO systems with single user, where we consider both phase noise caused by the imperfect transceivers and IRS. To this end, we propose a linear minimum mean square error (LMMSE) channel estimation algorithm that takes the phase noise into account. Furthermore, we study the impact of phase noise on the downlink performance of an IRS-assisted system. Both analytical and numerical results are presented, where we prove that the transceiver phase noise can be compensated with the optimized IRS, while the IRS phase noise remains but is not exacerbated. As the number of reflective elements <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> approaches infinity, the IRS phase noise results in a constant loss in terms of the ergodic rate. Moreover, we find that the phase noise has no impact on the scaling laws. If the direct channel is blocked and as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> → ∞, the transmit power can be scaled down by 1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> and 1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, for random and optimized IRS, without compromising the received signal to noise ratio (SNR).

Secure Transmission by Leveraging Multiple Intelligent Reflecting Surfaces in MISO Systems

Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user MISO systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternating optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. Compared to the traditional single IRS scheme, the proposed scheme can achieve better performance at the range of 2.4-6.4 bps/Hz with the increase in the number of reflecting elements in the multi-user scenarios. We also evaluate the gap between the secrecy rate for our proposed scheme under continuous phase shift/amplitude control and discrete phase shift/amplitude control, and our results show that the secrecy rate obtained from discrete approximation method converges to that achieved from the proposed scheme when increasing the discretization granularity.

Vector Coded Caching Multiplicatively Increases the Throughput of Realistic Downlink Systems

The recent introduction of vector coded caching has revealed that multi-rank transmissions in the presence of receiver-side cache content can dramatically ameliorate the file-size bottleneck of coded caching and substantially boost performance in error-free wire-like channels. In this work, we employ large-matrix analysis to explore the effect of vector coded caching in realistic wireless multi-antenna downlink systems. For a given downlink MISO system already optimized to exploit both multiplexing and beamforming gains, and for a fixed set of antenna and SNR resources, our analysis answers a simple question: What is the multiplicative throughput boost obtained from introducing reasonably-sized receiver-side caches that can pre-store information content? The derived closed-form expressions capture various linear precoders, and a variety of practical considerations such as power dissemination across signals, realistic SNR values, as well as feedback costs. The schemes are very simple (we simply collapse precoding vectors into a single vector), and the recorded gains are notable. For example, for 32 transmit antennas, a received SNR of 20 dB, a coherence bandwidth of 300 kHz, a coherence period of 40 ms, and under realistic file-size and cache-size constraints, vector coded caching is here shown to offer a multiplicative throughput boost of about 310% with ZF/RZF precoding and a 430% boost in the performance of already optimized MF-based (cacheless) systems. Interestingly, vector coded caching also accelerates channel hardening to the benefit of feedback acquisition, often surpassing 540% gains over traditional hardening-constrained cacheless downlink systems.

Joint User Scheduling and Phase Shift Design for RIS Assisted Multi-Cell MISO Systems

This letter investigates the joint user scheduling and phase shift design for reconfigurable intelligent surface (RIS) assisted multi-cell downlink systems. A closed-form ergodic sum spectral efficiency (SE) approximation is utilized as the optimization metric. Based on this approximation, we schedule the users, whose cascaded channels are mostly correlated with each other’s, to maximize each user’s effective signal. Moreover, the RIS phase shift is designed to be the mean of the scheduled users’ cascaded channel phases. With the proposed transmission design, we find the optimal RIS deployment to achieve the highest maximum throughput which depends only on the relative locations of the BSs and RIS. In addition, we consider a more practical discrete RIS phase shift design based on a discrete Fourier transform (DFT) codebook. Simulation results show that the proposed low-complexity scheduling algorithm performs well.

A Brief Review of Massive MIMO Technology for the Next Generation

Massive Multiple Input Multiple Output (MIMO) is an evolving technology based on the principle of spatial multiplexing winch consisting in using at the same time the same radio frequencies to send different signals. The several transmitting antennas from a base station can transmit different signals and several receiving antennas from a device can receive and divide them simultaneously. Due to the physically difficult of installing antennas close to each other, standard MIMO networks generally limit four antenna-side transmitters and receivers for data transmission while it could be more. The study aims to review the traditional MIMO different types as well as investigates the SNR between Single Input Single Output (SISO) and MIMO to ensure the best wireless connection functionality. In addition to that, a simple comparison to distinguish between SISO, SIMO, MISO, and MIMO in term of capacity and data rate to provide an indication for the quality of the wireless connection. The work's contribution is to illustrate technological benefits like MIMO, which boosts data speeds and increases the reliability of wireless networks. The outcome shows a SISO system would have a lower data rate than other systems because it only has one antenna at the transmitter and receiver, whereas a MISO system would typically have a greater Signal-to-Noise Ratio (SNR) than a SISO or SIMO system because it uses several transmit antennas. MIMO, however, took advantage of all the positive characteristics and emerged as the best solution overall.

Deep learning based beamforming for MISO systems with dirty‐paper coding

Deep learning based beamforming for MISO systems with dirty‐paper coding

Continual Learning-Based Fast Beamforming Adaptation in Downlink MISO Systems

Beamforming is an effective way to improve spectrum efficiency in multi-antenna systems. However, conventional iterative algorithms suffering from high computational delay renders beamforming solutions outdated and deep learning (DL) based realtime solutions suffer from the “task mismatch” problem, leading to severe performance deterioration. To resolve these issues, this letter proposes a beamforming adaptation scheme, named continual learning based beamforming neural network (CL-BNN), which can continuously learn and optimize the downlink beamforming in dynamic environment. The proposed CL-BNN scheme is model-driven and uses domain/expert knowledge to reduce training complexity. By combining our customized selection criteria into the structure of the CL-BNN, which selects the appropriate samples from the existing data and newly received data into a memory set for model training, taking into account the objective function, such that the tradeoff between maintaining performance of the trained task and adapting to the new task is achieved. Simulation results show that the proposed CL-BNN scheme achieves good adaptability in dynamic environments with low complexity.

Intelligent Channel Learning Exploiting Practical Energy Harvesting for Wireless MISO Systems

This article proposes a novel practical energy harvesting (EH) model-assisted deep learning framework for intelligent channel tracking. Specifically, a multiantenna wireless system is considered for energy beamforming in a nonlinear model-based EH scenario. Deep autoencoder technique is utilized for learning the channel characteristics due to nonconvexity of the channel estimation optimization problem. The performance evaluation is validated in low signal-to-noise ratio regimes, thereby providing key optimal design insights. Numerical results depict an overall performance enhancement as compared with existing benchmarks.

Joint Placement and Beamforming Design for IRS-Enhanced Multiuser MISO Systems

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments

In this paper, we study a reconfigurable intelligent surface (RIS)-aided multiuser MISO system with imperfect hardware, where the transceiver design is based on the statistical channel state information (CSI). Considering the transceiver hardware impairments (HWI), we aim to maximize the minimum average user data rate, where the precoding matrices at the base station (BS) and the reflecting phase shifts at the RIS are jointly optimized. Since the problem is nonconvex and the objective function cannot be derived in closed form, we adopt the deep deterministic policy gradient (DDPG) algorithm to deal with this challenging optimization problem, where we generate a set of CSI vectors in an offline way, and then these data sets are used to train the neural networks. The simulation results demonstrate the rapid convergence speed of the adopted DDPG algorithm and also emphasize that it is crucial to consider the HWI when optimizing the transceiver.

Precoding Design for Multi-User MISO Systems With Delay-Constrained and -Tolerant Users

In both academia and industry, multi-user multiple-input single-output (MU-MISO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MISO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MISO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MISO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MISO network to the multiple time slots scenario and propose a recursive GPI precoding algorithm. In simulation results, we validate proposed methods outperform baseline schemes and present the effect of network parameters on the average sum spectral efficiency.