Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks

We study the downlink linear precoder design problem in a multi-cell dense heterogeneous network (HetNet). The problem is formulated as a general sum-utility maximization (SUM) problem, which includes as special cases many practical precoder design problems such as multi-cell coordinated linear precoding, full and partial per-cell coordinated multi-point transmission, zero-forcing precoding and joint BS clustering and beamforming/precoding. The SUM problem is difficult due to its non-convexity and the tight coupling of the users' precoders. In this paper we propose a novel convex approximation technique to approximate the original problem by a series of convex subproblems, each of which decomposes across all the cells. The convexity of the subproblems allows for efficient computation, while their decomposability leads to distributed implementation. {Our approach hinges upon the identification of certain key convexity properties of the sum-utility objective, which allows us to transform the problem into a form that can be solved using a popular algorithmic framework called BSUM (Block Successive Upper-Bound Minimization).} Simulation experiments show that the proposed framework is effective for solving interference management problems in large HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio

Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

This paper addresses coordinated downlink beamforming optimization in multicell time-division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201

Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.Comment: 37 pages, 9 figures, accepted for publication in the IEEE Transactions on Wireless Communication

Revisiting the Energy-Efficient Hybrid D-A Precoding and Combining Design For mm-Wave Systems

Hybrid digital to analog (D-A) precoding is widely used in millimeter wave systems to reduce the power consumption and implementation complexity incurred by the number of radio frequency (RF) chains that consume a lot of the transmitted power in this system. In this paper, an optimal number of RF chains is proposed to achieve the desired energy efficiency (EE). Here, the optimization problem is formulated in terms of fractional programming maximization, resulting in a method with a twofold novelty: First, the optimal number of RF chains is determined by the proposed bisection algorithm, which results in an optimized number of data streams. Second, the optimal analog precoders/combiners are designed by eigenvalue decomposition and a power iteration algorithm, followed by the digital precoders/combiners which are designed based on the singular value decomposition of the proposed effective uplink and downlink channel gains. Furthermore, the proposed D-A systems are designed carefully to attain a lower complexity than the existing D-A algorithms while achieving reasonable performance. Finally, the impact of utilizing a different number of quantized bits of resolution on the EE is investigated. Simulation results show that the proposed algorithms outperform existing algorithms in terms of EE, spectral efficiency, and computational complexity

Intelligent Interactive Beam Training for Millimeter Wave Communications

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms

Physical Layer Anonymous Communications: An Anonymity Entropy Oriented Precoding Design (Invited Paper)

Different from traditional security-oriented designs, the aim of anonymizing techniques is to mask users' identities during communication, thereby providing users with unidentifiability and unlinkability. The existing anonymizing techniques are only designated at upper layers of networks, ignoring the risk of anonymity leakage at physical layer (PHY). In this paper, we address the PHY anonymity design with focus on a typical uplink scenario where the receiver is equipped with more antennas than the sender. With the increased degrees-of-freedom at the receiver side, we first propose a maximum likelihood estimation (MLE) signal trace-back detector, which only analyzes the signaling pattern of the received signal to disclose the sender's identity. Accordingly, an anonymity entropy anonymous (AEA) precoder is proposed, which manipulates the transmitted signalling pattern to counteract the receiver's trace-back detector and meanwhile to guarantee high receive signal-to-interference-plus-noise ratio for communication. More importantly, more data streams can be multiplexed than the number of transmit antennas, which is particularly suitable for the strong receiver configuration. Simulation demonstrates that the proposed AEA precoder can simultaneously provide high anonymity and communication performance

UL-DL duality for cell-free massive MIMO with per-AP power and information constraints

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal using limited cooperation capabilities. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial generalization of a previous result by Yu and Lan for deterministic channels. This generalization allows us to study involved information constraints going beyond the simple case of cluster-wise centralized precoding covered by previous techniques. Specifically, we show that the optimal joint precoders are, in general, given by an extension of the recently developed team minimum mean-square error method. As a particular yet practical example, we then solve the problem of optimal local precoding design in user-centric cell-free massive MIMO networks subject to per-AP power constraints

MSE minimized joint transmission in coordinated multipoint systems with sparse feedback and constrained backhaul requirements

In a joint transmission coordinated multipoint (JT-CoMP) system, a shared spectrum is utilized by all neighbor cells. In the downlink, a group of base stations (BSs) coordinately transmit the users’ data to avoid serious interference at the users in the boundary of the cells, thus substantially improving area fairness. However, this comes at the cost of high feedback and backhaul load; In a frequency division duplex system, all users at the cell boundaries have to collect and send feedback of the downlink channel state information (CSI). In centralized JT-CoMP, although with capabilities for perfect coordination, a central coordination node have to send the computed precoding weights and corresponding data to all cells which can overwhelm the backhaul resources. In this paper, we design a JT-CoMP scheme, by which the sum of the mean square error (MSE) at the boundary users is minimized, while feedback and backhaul loads are constrained and the load is balanced between BSs. Our design is based on the singular value decomposition of CSI matrix and optimization of a binary link selection matrix to provide sparse feedback—constrained backhaul link. For comparison, we adopt the previously presented schemes for feedback and backhaul reduction in the physical layer. Extensive numerical evaluations show that the proposed scheme can reduce the MSE with at least 25 % , compared to the adopted and existing schemes

Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August 14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use