Large-scale Fading Coefficients Research Articles

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

Unsupervised Deep Learning for Power Control of Cell-Free Massive MIMO Systems

The problem of power control for the uplink and the downlink in cell-free massive multiple-input multiple-output (CF mMIMO) systems is considered in this paper. In order to achieve a balance between the conflicting requirements of good performance levels and of low computational complexity, we employ unsupervised learning based on deep learning (DL). The proposed power control strategies can work using as input the large scale fading coefficients only and are capable to obtain very satisfactory performance levels, as compared to conventional, highly complex, optimization methods and to heuristic methodologies. Moreover, they can be used also in a user centric system wherein each mobile station is served by a subset of the active access points.

Deep Reinforcement Learning-Based Sum Rate Fairness Trade-Off for Cell-Free mMIMO

The uplink of a cell-free massive multiple-input multiple-output with maximum-ratio combining (MRC) and zero-forcing (ZF) schemes are investigated. A power allocation optimization problem is considered, where two conflicting metrics, namely the sum rate and fairness, are jointly optimized. As there is no closed-form expression for the achievable rate in terms of the large scale-fading (LSF) components, the sum rate fairness trade-off optimization problem cannot be solved by using known convex optimization methods. To alleviate this problem, we propose two new approaches. For the first approach, a use-and-then-forget scheme is utilized to derive a closed-form expression for the achievable rate. Then, the fairness optimization problem is iteratively solved through the proposed sequential convex approximation (SCA) scheme. For the second approach, we exploit LSF coefficients as inputs of a twin delayed deep deterministic policy gradient (TD3), which efficiently solves the non-convex sum rate fairness trade-off optimization problem. Next, the complexity and convergence properties of the proposed schemes are analyzed. Numerical results demonstrate the superiority of the proposed approaches over conventional power control algorithms in terms of the sum rate and minimum user rate for both the ZF and MRC receivers. Moreover, the proposed TD3-based power control achieves better performance than the proposed SCA-based approach as well as the fractional power scheme.

Cluster-Aided Collision Resolution Random Access in Distributed Massive MIMO Systems

Massive machine-type communication (MTC) is expected to be one of the key concerns in future B5G and 6G. However, the explosive growth of MTC devices (MTDs) in Internet of Things (IoT) poses great challenges for the initial access procedure. To address this issue, we propose a cluster-aided collision resolution random access (CACRRA) protocol for grant-based random access (RA) in the distributed massive multiple input multiple output (mMIMO) systems, which takes full advantage of the spatial multiplexing provided by distributed mMIMO. By exploiting the difference of large-scale fading coefficients between different access points (APs) and MTDs, we further propose an exclusive clustering algorithm to cluster APs for each pilot. Moreover, the pilot collision resolution is performed distributedly in each cluster by leveraging the channel hardening and the favorable propagation of mMIMO channels, which achieves a large reuse factor of pilots. Simulation results show that the proposed CACRRA can efficiently resolve the pilot collision and significantly improve the access performance. Specifically, the proposed protocol can improve the RA throughput more than nine times compared to the conventional protocol in incredibly crowded scenarios.

Blind Multiple Measurement Vector AMP Based on Expectation Maximization for Grant-Free NOMA

We consider a new approach to perform active user detection and channel estimation in massive grant-free access without requiring prior knowledge of the wireless channels, such as information on large-scale fading coefficients. To this end, we propose a multiple measurement vector approximate message passing (MMV-AMP) with expectation-maximization (EM)-based hyperparameter update, i.e., EM-MMV-AMP. Moreover, we revisited the decision rule for active user detection for EM-MMV-AMP. The numerical results indicate that the performance of the proposed scheme is superior to those of conventional schemes.

Large-Scale Fading Coefficients Classification-Aided Power Control Strategy for Mitigating the Pilot Contamination in Massive MIMO Systems

Deploying base stations with a massive number of antennas give birth to massive multi-input multi-output technology, which can boost the quality of the wireless communication; however, the limitations imposed by the length of the coherence interval has, directly, limited the number of the orthogonal pilot sequences (OPSs) that can be exploited for acquiring such accurate channel state information at each base station (BS). Hence, the available pilot resources are reused among several cells, which causes the problem of pilot contamination (PC). In this paper, the PC problem is analyzed, and by exploiting the large-scale fading coeffificients of the users’ equipment (UEs), we have classifified the users within each cell; therefore, the same OPS is, solely, reused by the far-distant UEs; hence, the inflfluence of the PC problem is made as small as possible. Since the UEs are subject to different channel conditions, we have consolidated our proposal by a power control algorithm, which boosts the performance of our proposal. Simulation results prove the effectiveness of our proposed decontaminating strategy.

Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.

Pilot Decontamination Processing in Cell-Free Massive MIMO

This letter focuses on the pilot contamination problem in the uplink and downlink of cell-free massive multiple-input multiple-output networks with different degrees of cooperation between access points. The optimum minimum mean square error processing can take advantage of large-scale fading coefficients for canceling the interference of pilot-sharing user-equipments and thus achieves asymptotically unbounded capacity. However, it is computationally demanding and can only be implemented in a fully centralized network. Here, sub-optimal schemes are derived that provide unbounded capacity with linear-growing complexity and using only local channel estimates but global channel statistics. This makes them suited for both centralized and distributed networks. In this latter case, the best performance is achieved with a generalized maximum ratio combiner that maximizes a capacity bound based on channel statistics only.

Pseudo-channel matrix truncation based spatial correlation mitigation in massive MIMO

Massive multiple-input multiple-output (MIMO), a technique that can greatly increase spectral efficiency (SE) of cellular networks, has attracted significant interests in recent years. One of the major limitations of massive MIMO systems is pilot contamination, which will deteriorate the SE. The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction. However, it cannot break through another limitation of massive MIMO, i.e., spatial correlation. In addition, it will also lead to interference between the pilot and user data since they are imposed together. In this paper, we try to tackle these two issues, which will be described as follows. Firstly, a column-wise asymptotically orthogonal matrix, named as pseudo-channel matrix, is developed by orthogonalization of received signal. To recover the information about the large-scale fading (LSF) coefficients, the pseudo-channel matrix is truncated according to the cardinality of adjacent users set (CAUS). By this means, spatial correlation can be mitigated effectively. Secondly, robust independent component analysis (RobustICA) is used to reduce the interference caused by user data, and as a result the system performance can be further improved. Numerical simulation results demonstrate the effectiveness of the proposed method.

Cell-Free Massive MIMO: Joint Maximum-Ratio and Zero-Forcing Precoder With Power Control

Cell-free massive multiple-input multiple-output (MIMO) system is a promising architecture for next generation wireless systems by deploying a very large number of distributed access points (APs), which simultaneously serve a smaller number of user equipments (UEs) over the same time-frequency resources. It guarantees uniformly good service at high spectral efficiency with simple linear precoding techniques and max-min power control. In this article, we propose a new joint maximum-ratio and zero-forcing (JMRZF) precoding scheme, where part of APs are combined to perform centralized zero-forcing (ZF), while other APs apply simple maximum-ratio transmission (MRT). Our proposed precoder offers an adaptable trade-off between the spectral efficiency and front-haul signalling overhead. A corresponding AP subset selection scheme is also proposed which is based on large-scale fading coefficients. A closed-form expression for the achievable spectral efficiency of our proposed scheme is derived, which represents a generalized result including both fully distributed MRT and fully centralized ZF cases. Based on this closed-form expression, max-min power control is formulated and solved via the second order cone and first order methods. The former can obtain the global optimal solution, but its computational complexity is very high. On the other hand, the latter technique is sub-optimal, yet, it has very low computational complexity. Hence, it is suitable for large-scale cell-free massive MIMO systems with hundreds or thousands of APs and users. Numerical results show that our proposed JMRZF scheme can substantially outperform the local precoding schemes, even when a small part of APs are combined to deploy ZF and is implementable even when each AP has very few antennas. In addition, it is shown that our max-min power controls improves the spectral efficiency significantly, compared to the uniform power control scheme.

Outage Probability Analysis of IRS-Assisted Systems Under Spatially Correlated Channels

This letter investigates the impact of spatial channel correlation on the outage probability of intelligent reflecting surface (IRS)-assisted single-input single-output (SISO) communication systems. In particular, we derive a novel closed-form expression of the outage probability for arbitrary phase shifts and correlation matrices of the indirect channels. To shed light on the impact of the spatial correlation, we further attain the closed-form expressions for two common scenarios met in the literature when the large-scale fading coefficients are expressed by the loss over a propagation distance. Numerical results validate the tightness and effectiveness of the closed-form expressions. Furthermore, the spatial correlation offers significant decreases in the outage probability as the direct channel is blocked.

A Scalable and Energy-Efficient IoT System Supported by Cell-Free Massive MIMO

An Internet-of-Things (IoT) system supports a massive number of IoT devices wirelessly. We show how to use cell-free (CF) massive multiple input and multiple output (MIMO) to provide a scalable and energy-efficient IoT system. We employ optimal linear estimation with random pilots to acquire channel state information (CSI) for MIMO precoding and decoding. In the uplink (UL), we employ optimal linear decoder and utilize random matrix (RM) theory to obtain two accurate signal-to-interference plus noise ratio (SINR) approximations involving only large-scale fading coefficients. We derive several max–min type power control algorithms based on both exact SINR expression and RM approximations. Next we consider the power control problem for downlink (DL) transmission. To avoid solving a time-consuming quasiconcave problem that requires repeat tests for the feasibility of a second-order cone programming (SOCP) problem, we develop a neural network (NN) aided power control algorithm that results in 30 times reduction in computation time. This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs. Both UL and DL power control algorithms allow visibly improve the system spectral efficiency (SE) and, more importantly, lead to multifold improvements in energy efficiency (EE), which is crucial for IoT networks.

A pilot allocation method for multi-cell multi-user massive MIMO system

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple out-put (MIMO) systems. This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency. The pilots of the terminals are allocated one-bit orthogonal identifier to diminish the cell categories by the operation of exclusive OR (XOR). At the same time, the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm, and different pilot sequences are assigned to different groups. The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Performance Analysis of Massive MIMO Relay Systems With Variable-Resolution ADCs/DACs Over Spatially Correlated Channels

The use of low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) is recognized as a practical technique to reduce power consumption and hardware cost for massive multiple-input and multiple-output (MIMO) systems. In this context, by leveraging on the additive quantization noise model (AQNM), this paper investigates the performance of massive MIMO relay systems with a variable-resolution ADC/DAC-based architecture, where each ADC and DAC use arbitrary bits for quantization. To be specific, we first derive the closed-form expression of achievable rate over spatially correlated Rayleigh fading channels by using perfect channel state information (CSI), maximal ratio combining (MRC), and maximal ratio transmission (MRT). Afterwards, we extend the work to the case of imperfect CSI. The analytical expressions reveal some insights of key parameters, i.e., spatial channel correlation, large-scale fading coefficients, estimate errors, and the resolution of ADCs/DACs. Finally, simulation results validate our theoretical analyses and confirm that the achievable rate reduces as the spatial correlation rises. Besides, we provide the condition under which coarse DACs (ADCs) and receive spatial correlation (transmit spatial correlation) dominate the loss of achievable rate. Moreover, provided that the total number of quantization bits is constant, we find that the achievable rate is optimal over spatially correlated channels when all ADC and DAC adopt the same quantization bits from the perspective of statistic.

Cooperative PSK constellation design and power allocation for massive MIMO uplink communications

This paper considers a massive multiple-input multiple-output (MIMO) wireless uplink communication system over Rayleigh fading channels. In this system, two single-antenna nodes timely upload data to a base station (BS) with a large number of antennas on the same time-frequency resources. The small scale fading coefficients keep constant during two consecutive time slots, after which they change into new independent values. Assuming only the large-scale fading coefficients are available, we construct a maximum likelihood (ML) detector without the instantaneous small scale fading coefficients at the BS. The key idea is to utilize the absolutely additively uniquely decomposable constellation pair with a phase-shift keying (PSK) modulation scheme. Specifically, we use a rotated PSK constellation for one of the nodes to ensure the unique identification of transmitted symbols in a noise-free case. In a noisy case, we propose a power allocation framework for the proposed modulation scheme to improve the ML detector's average symbol error performance. Finally, simulations results verify the effectiveness of our proposed scheme.

The harmful influence of employing a large number of pilot-resources on the performance of massive MIMO systems

One of the severe constraints that limit the implementation of massive multi-input multi-output (M-MIMO) systems is called pilot contamination (PC). To overcome this problem, soft pilot reuse (SPR) strategy was proposed, which aims to properly allocate the Pilot Sequences (PS) to the users based on their properties e.g., Quality-of-Service. Despite its flaws, which can damage the spectral efficiency, the SPR strategy has been, heavily, employed as a decontaminating strategy. To reveal the weakness of this approach, this paper adopts a SPR based-strategy that separates the users within cells based on their large scale fading coefficients, therefore, because the edge-users (EUs) are mostly affected by the PC compared to the centre-users (CUs), a set of orthogonal pilots is allocated to the EUs of the overall cells, while the CUs of each cell are obliged to reuse the same set of orthogonal pilots as the CUs of adjacent cells.

Pilot Assignment in Cell-Free Massive MIMO Based on the Hungarian Algorithm

This letter focuses on the problem of pilot assignment in cell-free massive MIMO systems. Exploiting the well-known Hungarian algorithms, two procedures are proposed, one maximizing the system throughput, and the other one maximizing the fairness across users. The algorithms operate based on the knowledge of large-scale fading coefficients as a proxy for the distances between users in the system, and take into account both the uplink and downlink performance. Numerical results show that the proposed pilot assignment algorithms are effective and outperform the many competing alternatives available in the literature.

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

Graph Coloring Based Pilot Assignment for Cell-Free Massive MIMO Systems

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences.

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design. Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.