Achievable Downlink Rate Research Articles

Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

Network-assisted full-duplex cell-free mmWave networks: Hybrid MIMO processing and multi-agent DRL-based power allocation

This paper investigates the network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) networks, where the distribution of the transmitting access points (T-APs) and receiving access points (R-APs) across distinct geographical locations mitigates cross-link interference, facilitating the attainment of a truly flexible duplex mode. To curtail deployment expenses and power consumption for mmWave band operations, each AP incorporates a hybrid digital–analog structure encompassing precoder/combiner functions. However, this incorporation introduces processing intricacies within channel estimation and precoding/combining design. In this paper, we first present a hybrid multiple-input multiple-output (MIMO) processing framework and derive explicit expressions for both uplink and downlink achievable rates. Then we formulate a power allocation problem to maximize the weighted bidirectional sum rates. To tackle this non-convex problem, we develop a collaborative multi-agent deep reinforcement learning (MADRL) algorithm called multi-agent twin delayed deep deterministic policy gradient (MATD3) for NAFD cell-free mmWave networks. Specifically, given the tightly coupled nature of both uplink and downlink power coefficients in NAFD cell-free mmWave networks, the MATD3 algorithm resolves such coupled conflicts through an interactive learning process between agents and the environment. Finally, the simulation results validate the effectiveness of the proposed channel estimation methods within our hybrid MIMO processing paradigm, and demonstrate that our MATD3 algorithm outperforms both multi-agent deep deterministic policy gradient (MADDPG) and conventional power allocation strategies.

Performance analysis of full-duplex densely distributed MIMO with wireless backhaul

In this paper, the spectral efficiency of a full-duplex (FD) densely distributed multi-input multi-output (MIMO) system with wireless backhaul is considered. In-band full-duplex (IBFD) backhauling, in which the backhaul and access transmissions take place on the same spectrum, is exploited for wireless backhauling to enable an efficient spectrum reuse. However, the severe cross-tier interference and cross-link interference reduce the gains of IBFD backhauling. To evaluate the achievable spectral efficiency with imperfect channel state information (CSI), we propose a two-phase channel estimation scheme to estimate the CSI for two wireless links, and the scheme estimates an effective interference CSI between access points (APs) based on beamforming training to perform interference cancelation at APs. Given the estimated CSI, the closed-form expressions of the uplink and downlink achievable rates with maximum ratio transmission beamforming and maximum ratio combining receivers, respectively, are derived with Gamma approximation. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the two-phase channel estimation scheme for interference cancelation. Moreover, compared with half-duplex densely distributed MIMO systems, FD systems with interference cancelation have a better performance.

Spectral Efficiency of Unicast and Multigroup Multicast Transmission in Cell-Free Distributed Massive MIMO Systems

In this article, we consider a joint unicast and multi-group multicast cell-free distributed massive multiple-input multiple-output (MIMO) system, while accounting for co-pilot assignment strategy based channel estimation, pilot contamination and different precoding schemes. Under the co-pilot assignment strategy, we derive the minimum-mean-square error (MMSE) channel state information (CSI) estimation for unicast and multicast users. Given the acquired CSI, the closed-form expressions for downlink achievable rates with maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are derived. Based on these expressions, we propose an efficient power allocation scheme by solving a multi-objective optimization problem (MOOP) between maximizing the minimum spectral efficiency (SE) of multicast users and maximizing the average SE of unicast users with non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning (DL) problem and solved by an unsupervised learning method to further promote computational efficiency. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the joint unicast and multigroup multicast transmission scheme in cell-free distributed massive MIMO systems. The SE analysis under various system parameters and the trade-off regions between these two conflicting optimization objectives offers numerous flexibilities for system optimization.

Two‐timescale design for RIS‐aided full‐duplex MIMO systems with transceiver hardware impairments

This paper focuses on a reconfigurable intelligent surface (RIS)-aided full-duplex multi-user massive multiple-input multiple-output system with transceiver hardware impairments (THWIs). Different from the existing works, the two-timescale design scheme is considered. The phase shift at the RIS is designed only based on the statistical channel state information. Firstly, the closed-form expression of the uplink and downlink achievable rate is derived. Then, the power scaling laws are revealed. Finally, the impact of THWIs on the system performance is analysed and genetic algorithm to maximize the achievable rate is used.

Downlink Achievable Rate of D2D Underlaid Cell-Free Massive MIMO Systems With Low-Resolution DACs

Device-to-device (D2D) communication coexisted with a cell-free massive multiple-input multiple-output (MIMO) system will be one of the pieces of the B5G/6G jigsaw puzzle. In this article, a D2D underlaid cell-free massive MIMO system with low-resolution digital-to-analog converters is studied, where multiantenna D2D user equipments (DUEs) communicate pairwise with each other in the same time-frequency resources as cell-free with equipments (CFUEs). Assuming imperfect channel state information is available at access points (APs), the downlink closed-form achievable rates for both CFUEs and multiantenna DUEs are derived, which enable us to give insights into the impacts of the key design parameters (e.g., the AP number, the DUE density, and its antenna number) on achievable rate. After that, a power control scheme is developed to maximize the average sum rate of CFUEs and DUEs, and we show that the proposed power control problem can be effectively solved by using the path-following algorithm. Finally, numerical simulations are provided to highlight the system performance and verify the effectiveness of the proposed algorithm.

RIS-assisted cell-free massive MIMO systems with reflection area: AP number reduction

Cell-free massive multiple-input multiple-output (MIMO) has been regarded as a promising technology due to high spectral efficiency. However, as large number of access points (APs) are deployed with fibers connecting to the central processing unit, the increase of energy consumption and hardware cost raise concerns. The reconfigurable intelligent surface (RIS) with impressing potential for low energy and cost finds a way to solve this problem. In this paper, we investigate the performance of the cell-free massive MIMO system with a RIS. As RIS can only reflect signals from the front, user equipments (UEs) and APs are divided into two categories according to their relative position with the RIS, i.e., one is on the reflection area of the RIS and the other is not. A closed-form approximation of the UE achievable downlink rate is derived. Based on it, we obtain the optimal RIS position and phase shift that can maximize the UE sum rate, through alternating optimization method. It is found that compared with the cell-free massive MIMO system without RIS, to achieve the same rate performance, the number of required AP in the RIS-assisted system can be significantly reduced. Moreover, as the RIS component increases, the number of required AP can be reduced almost linearly without rate reduction, which means the hardware and energy cost can be greatly cut down. Furthermore, from our simulation results, we can see that when users are densely distributed, the optimal location of the RIS should be closer to users. When users are uniformly distributed, the optimal position of RIS is close to the central position.

Impact of Phase-Noise and Spatial Correlation on Double-RIS-Assisted Multiuser MISO Networks

We study the performance of a phase-noise impaired double reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input single-output (MISO) system under spatial correlation at both RISs and base-station (BS). The downlink achievable rate is derived in closed-form under maximum ratio transmission (MRT) precoding. In addition, we obtain the optimal phase-shift design at both RISs in closed-form for the considered channel and phase-noise models. Numerical results validate the analytical expressions, and highlight the effects of different system parameters on the achievable rate. Our analysis shows that phase-noise can severely degrade the performance when users do not have direct links to both RISs, and can only be served via the double-reflection link. Also, we show that high spatial correlation at RISs is essential for high achievable rates.

Robust Downlink Transmission for 6G LEO-MIMO Satellite Systems

In this paper, we investigate downlink transmission schemes robust to the Doppler frequency offset (DFO) induced by high mobility for low earth orbit multiple-input and multiple-output satellite communication systems. Considering the impact of DFO, we derive the closed-form expressions for the downlink achievable rates with maximum ratio transmission (MRT) and maximum average signal-to-leakage-plus-noise ratio (MASLNR) precoding. Numerical results show that the derived closed-form expressions are accurate and the DFO severely affects system performance. In order to relieve the impact of DFO, we propose an adaptive DFO compensation algorithm based on the precompensation design and beam alignment. Numerical results verify the effectiveness of the proposed DFO compensation algorithm.

Improved Downlink Rates for FDD Massive MIMO Systems Through Bayesian Neural Networks-Based Channel Prediction

In frequency-division-duplex (FDD) massive MIMO systems, channel state information (CSI) feedback waiting phase does not get fully exploited since base station needs to wait for the CSI feedback before transmitting downlink data. The proportion of the CSI feedback waiting phase during the downlink transmission would be high as the MIMO system scales up, which sacrifices downlink rates of the FDD massive MIMO systems significantly. In this paper, we first present a channel prediction-aided FDD scheme to utilize the idle waiting time efficiently. Then we propose a novel channel prediction method based on Bayesian neural network (BNN), which can handle the uncertainty in a natural manner and learn regularization from data without painstaking manual pre-tuning of network hyperparameters. Numerical results show that our proposed channel prediction-aided FDD scheme can achieve remarkable performance gains in terms of either achievable downlink rates or bit error rate. Moreover, our proposed BNN-based channel predictor is much more effective and robust in contrast to the state-of-the-art channel prediction techniques such as autoregressive model and recurrent neural network.

Downlink Analysis for the D2D Underlaid Multigroup Multicast Cell-Free Massive MIMO With Low-Resolution ADCs/DACs

To fully utilize the scarce spectrum resources, the downlink device-to-device (D2D) underlaid multigroup multicast cell-free massive multi-input multi-output (MIMO) systems is proposed, where the geographically distributed access points (APs) serving both multi-antenna cell-free users (CFUEs) and D2D users (DUEs) pair over the same time/frequency resource blocks. Specially, the low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) are adopted at the AP to effectively reduce hardware damage and energy costs. The imperfect channel state information (CSI) is obtained firstly by the standard minimum mean square error (MMSE), then the lower bounds of the closed-form achievable downlink rates for both CFUEs and DUEs are derived. Aside from that, the sum energy efficiency (EE) is also explored using the established power consumption model. Finally, numerical simulations are assessed to verify the analytical results and provide insights into the effects of the key system parameters. It demonstrates that increasing the number of APs can improve the sum SE, however, the sum EE decreases with the number of the APs sharply. Furthermore, increasing the density of DUEs can significantly improve the system capacity. The findings highlight the significance of the collaborative deployment of D2D and multigroup multicast cell-free massive MIMO technology, which can serve as a valuable reference and guide for the actual deployment of future communication systems.

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels.

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

Reconfigurable Intelligent Surface Aided Massive MIMO Systems With Low-Resolution DACs

We investigate a reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multi-output (MIMO) system where low-resolution digital-analog converters (DACs) are configured at the base station (BS) in order to reduce the cost and power consumption. An approximate analytical expression for the downlink achievable rate is derived based on maximum ratio transmission (MRT) and additive quantization noise model (AQNM), and the rate maximization problem is solved by particle swarm optimization (PSO) method under both continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS. Simulation results show that the downlink sum achievable rate tends to a constant with the increase of the number of quantization bits of DACs, and four quantization bits are enough to capture a large portion of the performance of the ideal perfect DACs case.

A Post-Coder Feedback Approach to Overcome Training Asymmetry in MIMO-TDD

Time Division Duplex (TDD) wireless communication systems are inherently bidirectional, which facilitates exploiting channel reciprocity for pilot based channel estimation of both uplink and downlink. However, there exists a gross asymmetry in channel estimation complexity for the uplink and downlink, particularly for Multiple Input Multiple Output (MIMO) TDD systems. Usually, Base Stations (BS) with more antennas need to estimate fewer parameters from each antenna, whereas the estimation requirement is disproportionately higher at the User Equipment (UE). Unlike the UE, the BS has powerful hardware, computational resources and energy to estimate and track channel profiles accurately. To overcome this asymmetry, we propose a solution for MIMO-TDD downlink communication, wherein the post-coder part of the channel matrix is quantized at the BS, and is communicated to the UE via a low-rate channel. Using asymptotically tight lower bounds on the downlink information-theoretic rates achievable, we quantify the performance of the proposed scheme. Simulations reveal that a moderate number of quantization bits are sufficient to achieve rates close to the link capacity. We further show that in the massive MIMO regime, where the BS has many more antennas than the UE, the channel can be effectively compensated by appropriate transmit domain precoding, without any post-coder knowledge at the UE.

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

An energy-efficient power allocation scheme for Massive MIMO systems with imperfect CSI

As a design goal of green wireless communication, it is an important consideration to maximize energy efficiency of a Massive multiple-input multiple-output (MIMO) downlink system. By considering the case where the base station and all the users have the imperfect channel state information, a power allocation optimization problem is formulated to achieve the maximization of energy efficiency. Also, we derive the tractable closed-form expressions of lower bound on the achievable downlink rate utilizing maximum-ratio transmission (MRT) and zero-forcing precoding. Since the proposed optimization problem is non-convex, we strive to find a sub-optimal power allocation scheme. By decomposing the expression of the non-convex objective function, an energy-efficient power allocation algorithm that maximizes the energy efficiency is developed based on the difference of convex programming. Numerical results verify the tightness of the derived closed-form expressions, and show the performance improvement of the proposed energy-efficient power allocation scheme over the existing power allocation schemes.

Cell-Free Massive MIMO Systems With Low-Resolution ADCs: The Rician Fading Case

This article studies the uplink and downlink achievable rates of cell-free massive multi-input multi-output (MIMO) systems over Rician fading channels, assuming that each access point possesses multiple antennas that are connected with low-resolution analog-to-digital converters (ADCs). Achievable closed-form rate expressions for both uplink and downlink are derived, which enable us to explore the design issues surrounded by the key parameters, such as the number of total antennas, the transmit power, the ADC resolution, and the Rician <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {{\it K}}$</tex-math></inline-formula> -factor. Moreover, the weighted max–min fairness power optimization algorithms are proposed. Specifically, the proposed algorithms for uplink and downlink can be formulated as geometric programming and second-order-cone programming, respectively. All the theoretical analyses and the effectiveness of the proposed algorithms are verified by simulation experiments.

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

The increasing demand for higher data rates motivates the exploration of advanced techniques for future wireless networks. To this end, massive multiple-input multiple-output (mMIMO) is envisioned as the most essential technique to meet this demand. However, the expansion of the number of antennas in mMIMO systems with short coherence time makes the downlink channel estimation (DCE) overhead potentially overwhelming. As such, the number of training sequence (TS) needs to be significantly reduced. However, reducing the number of TS reduces the mean-squared error (MSE) accuracy significantly and to date it is not clear to what extend can this TS reduction affects the achievable sum rate performance. Therefore, this paper develops a low complexity and tractable TS solution for DCE and establishes an analytical framework for the optimum TS. Furthermore, the tradeoff between the achievable sum rate maximization criteria and the MSE minimization criteria is investigated. This investigation is essential to characterize the optimum TS length and the actual performance of mMIMO systems when the channel exhibits a limited coherence time. To this end, the statistical structure of mMIMO channels is exploited. In addition, this paper utilizes a random matrix theory (RMT) method to characterize the downlink achievable sum rate and MSE in a closed-form. This paper shows that maximizing the downlink sum rate criterion is more important than minimizing the MSE of the SINR only, which is typically considered in the conventional MIMO systems and/or in the time division duplex (TDD) mMIMO systems. The results demonstrate that a feasible downlink achievable sum rate can be achieved in an frequency division duplex (FDD) mMIMO system. This finding is necessary to extend the benefit of mMIMO systems to high frequency bands such as millimeter-wave (mmWave) and Terahertz (THZ) communications.

Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise

Massive multiple-input multiple-output (MaMi) systems have attracted much research attention during the last few years. This is because MaMi systems are able to achieve a remarkable improvement in data rate and thus meet the immensely ongoing traffic demands required by the future wireless networks. To date, the downlink training sequence (DTS) for the frequency division duplex (FDD) MaMi communications systems have been designed based on the idealistic assumption of white noise environments. However, it is essential and more practical to consider the colored noise environments when designing an efficient DTS for channel estimation. To this end, this paper proposes a new DTS design by exploring the joint use of spatial channel and noise covariance matrices, when the channel is not reciprocal but the coherence block length remains limited. We derive an analytical solution for the mean square error (MSE) based on the proposed training design with colored noise. In addition, this paper exploits the method of random matrix theory to provide an analytical solution for the downlink (DL) achievable sum rate of the regularized zero forcing beamforming (RZFBF) precoder. Numerical results demonstrate that using the proposed DTS design, the MSE of the channel estimate is significantly reduced compared with the conventional training designs with white noise. Furthermore, the results show that the proposed pilot design markedly improves the DL achievable SR over the conventional training designs, especially at relatively low signal-to-noise-ratio (SNR) levels. This enables FDD MaMi systems to operate under more practical scenarios of colored noise and limited coherence time environments.