Robust Congestion Control for Demand-Based Optimization in Precoded Multi-Beam High Throughput Satellite Communications

in IEEE Transactions on Communications (2022)

Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems

in IEEE Transactions on Communications (2022), 70(7), 4359-4372

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest ... [more ▼]

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios. [less ▲]

Toward Optimally Efficient Search With Deep Learning for Large-Scale MIMO Systems

in IEEE Transactions on Communications (2022), 70(5), 3157-3168

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by ... [more ▼]

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats. [less ▲]

On the Secrecy Capacity of MIMO Wiretap Channels: Convex Reformulation and Efficient Numerical Methods

in IEEE Transactions on Communications (2021), 69(10), 6865-6878

This paper presents novel numerical approaches to finding the secrecy capacity of the multiple-input multiple-output (MIMO) wiretap channel subject to multiple linear transmit covariance constraints ... [more ▼]

This paper presents novel numerical approaches to finding the secrecy capacity of the multiple-input multiple-output (MIMO) wiretap channel subject to multiple linear transmit covariance constraints, including sum power constraint, per antenna power constraints and interference power constraint. An analytical solution to this problem is not known and existing numerical solutions suffer from slow convergence rate and/or high per-iteration complexity. Deriving computationally efficient solutions to the secrecy capacity problem is challenging since the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix, for which its convexity is only known for some special cases. In this paper we propose two low-complexity methods to compute the secrecy capacity along with a convex reformulation for degraded channels. In the first method we capitalize on the accelerated DC algorithm which requires solving a sequence of convex subproblems, for which we propose an efficient iterative algorithm where each iteration admits a closed-form solution. In the second method, we rely on the concave-convex equivalent reformulation of the secrecy capacity problem which allows us to derive the so-called partial best response algorithm to obtain an optimal solution. Notably, each iteration of the second method can also be done in closed form. The simulation results demonstrate a faster convergence rate of our methods compared to other known solutions. We carry out extensive numerical experiments to evaluate the impact of various parameters on the achieved secrecy capacity. [less ▲]

Impact of Varying Radio Power Density on Wireless Communications of RF Energy Harvesting Systems

in IEEE Transactions on Communications (2020)

Hybrid User Pairing for Spectral and Energy Efficiencies in Multiuser MISO-NOMA Networks with SWIPT

in IEEE Transactions on Communications (2020), 68(8), 4874-4890

In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output nonorthogonal multiple access networks with simultaneous wireless information and power ... [more ▼]

In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output nonorthogonal multiple access networks with simultaneous wireless information and power transfer. In this system, two information users with distinct channel conditions are optimally paired while energy users perform energy harvesting (EH) under non-linearity of the EH circuits. We consider the problem of jointly optimizing user pairing and power allocation to maximize the overall spectral efficiency (SE) and energy efficiency (EE) subject to userspecific quality-of-service and harvested power requirements. A new paradigm for the EE-EH trade-off is then proposed to achieve a good balance of network power consumption. Such design problems are formulated as the maximization of nonconcave functions subject to the class of mixed-integer non-convex constraints, which are very challenging to solve optimally. To address these challenges, we first relax binary pairing variables to be continuous and transform the design problems into equivalent non-convex ones, but with more tractable forms. We then develop low-complexity iterative algorithms to improve the objectives and converge to a local optimum by means of the inner approximation framework. Simulation results show the convergence of proposed algorithms and the SE and EE improvements of the proposed HUP scheme over state-of-the-art designs. In addition, the effects of key parameters such as the number of antennas and dynamic power at the BS, target data rates, and energy threshold, on the system performance are evaluated to show the effectiveness of the proposed schemes in balancing resource utilization. [less ▲]

Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

in IEEE Transactions on Communications (2019), 67(11), 8124-8136

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station ... [more ▼]

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori , and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies. [less ▲]

Transceiver design of optimum wirelessly powered full-duplex MIMO IoT devices

in IEEE Transactions on Communications (2018), 66(5), 1955-1969

Impact of Residual Additive Transceiver Hardware Impairments on Rayleigh-Product MIMO Channels with Linear Receivers: Exact and Asymptotic Analyses

in IEEE Transactions on Communications (2017)

Adaptive Compression and Joint Detection for Fronthaul Uplinks in Cloud Radio Access Networks

in IEEE Transactions on Communications (2015), 63(11), 4565-4575

Cloud radio access network (C-RAN) has recently attracted much attention as a promising architecture for future mobile networks to sustain the exponential growth of data rate. In C-RAN, one data ... [more ▼]

Cloud radio access network (C-RAN) has recently attracted much attention as a promising architecture for future mobile networks to sustain the exponential growth of data rate. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), which are connected to the BBU via high capacity, low latency fronthaul links. In this paper, we study the compression on fron- thaul uplinks and propose a joint decompression algorithm at the BBU. The central premise behind the proposed algorithm is to ex- ploit the correlation between RRHs. Our contribution is threefold. First, we propose a joint decompression and detection (JDD) algorithm which jointly performs decompressing and detecting. The JDD algorithm takes into consideration both the fading and compression effect in a single decoding step. Second, block error rate (BLER) of the proposed algorithm is analyzed in closed-form by using pair-wise error probability analysis. Third, based on the analyzed BLER, we propose adaptive compression schemes subject to quality of service (QoS) constraints to minimize the fronthaul transmission rate while satisfying the pre-defined target QoS. As a dual problem, we also propose a scheme to minimize the signal distortion subject to fronthaul rate constraint. Numerical re- sults demonstrate that the proposed adaptive compression schemes can achieve a compression ratio of 300% in experimental setups. [less ▲]