We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multi-user interfering systems. Our main contributions are i) the development of the first class of (inexact) Jacobi best-response algorithms with provable convergence, where all the users simultaneously and iteratively solve a suitably convexified version of the original sum-utility optimization problem; ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics; and iii) a framework that can be easily particularized to well-known applications, giving rise to very efficient practical (Jacobi or Gauss-Seidel) algorithms that outperform existing ad hoc methods proposed for very specific problems. Interestingly, our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descent schemes for convex functions.

pdf/decomposition-by-partial-linearization-parallel-optimization-2kejrg1ney.pdf

Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

Noncooperative game-theoretic tools have been increasingly used to study many important resource allocation problems in communications, networking, smart grids, and portfolio optimization. In this paper, we consider a general class of convex Nash equilibrium problems (NEPs), where each player aims at solving an arbitrary smooth convex optimization problem. Differently from most of current works, we do not assume any specific structure for the players' problems, and we allow the optimization variables of the players to be matrices in the complex domain. Our main contribution is the design of a novel class of distributed (asynchronous) best-response-algorithms suitable for solving the proposed NEPs, even in the presence of multiple solutions. The new methods, whose convergence analysis is based on variational inequality (VI) techniques, can select, among all the equilibria of a game, those that optimize a given performance criterion, at the cost of limited signaling among the players. This is a major departure from existing best-response algorithms, whose convergence conditions imply the uniqueness of the NE. Some of our results hinge on the use of VI problems directly in the complex domain; the study of these new kind of VIs also represents a noteworthy innovative contribution. We then apply the developed methods to solve some new generalizations of Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) games in cognitive radio systems, showing a considerable performance improvement over classical pure noncooperative schemes.

Real and Complex Monotone Communication Games

The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

Recent results have shown the benefits of widely linear precoding (WLP) in the MIMO interference channel (MIMO IC) assuming that all transmitters can follow the same strategy. Motivated by a transitional scenario where legacy linear transmitters coexist with widely linear ones, this work investigates the general $K$ -user MIMO IC in a heterogeneous (linear and widely linear) transmitter deployment. In particular, we address the maximization of the weighted sum-rate (WSR) for (widely) linear transmit filters design through the use of the complex-valued formulation. Since the maximum WSR problem is non-convex, and thus difficult to be solved, we formulate an equivalent minimum weighted mean square error problem that allows deriving closed-form expressions for (widely) linear transceivers. Then an iterative procedure is proposed, which is proven to reach a stationary point of the maximum WSR problem. Simulations show that the proposed procedure allows increasing the sum-rate as compared to coordinated linear transceiver schemes. The gains are larger and significant in two different nonexclusive conditions: as the interference level increases or when the number of antennas is low.

/pdf/coexisting-linear-and-widely-linear-transceivers-in-the-mimo-1w2oj7rbiv.pdf

Coexisting Linear and Widely Linear Transceivers in the MIMO Interference Channel

Cellular networks need the densification of small eNBs (SeNBs) to face the tremendous data traffic demand growth, implying an interference increase and making transmit coordination a key enabler. This article proposes a decentralized coordinated precoding (D-CoP) for downlink (DL) weighted sum-rate maximization in dense MIMO TDD small cell networks (SCNs). Each SeNB designs its own precoding matrices based on channel state information (CSI) of the served users and knowledge of the interference-cost matrix that allows managing interference towards unintended users. A protocol is proposed to acquire the interference-cost matrix by processing the uplink (UL) received signal provided that: 1) channel reciprocity can be assumed and 2) all users participating in DL can transmit in UL with an adequate transmit filter. In contrast to existing transmit coordination techniques, D-CoP is fully scalable, avoids estimation of the interfering channels, and does not require information exchange between SeNBs. In case all parameters are perfectly acquired, an iterative algorithm is presented with demonstrated monotonic convergence when all SeNBs update its transmit precoders simultaneously. Further, the problem is reformulated in order to derive a robust D-CoP under imperfect CSI conditions. Finally, simulations in 3GPP LTE-Advanced SCNs show significant user packet throughput gains, without increasing the complexity associated to transmit coordination. Robustness to imperfect CSI and non-ideal channel reciprocity is shown through simulations.

https://upcommons.upc.edu/bitstream/handle/2117/81143/TWC2422704.pdf?sequence=1&isAllowed=y

Decentralized Coordinated Precoding for Dense TDD Small Cell Networks

Distributed dynamic pricing for MIMO interfering multiuser systems: A unified approach

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Distributed dynamic pricing for MIMO interfering multiuser systems: A unified approach