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There is a trend that the functionalities of communication, computing, and caching (CC&C) are merging together in the future networks. Recently, this mergence is formed by the concept of cloud radio access network (C-RAN) with caching as a service (CaaS). In this paper, we dissect the interactions of CC&C in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension...
This paper considers a multiuser system with one full-duplex (FD) base station (BS) serving a set of half duplex (HD) mobile users. Due to the self-interference and co-channel interference from the uplink users to the downlink users, the transmissions of the BS and uplink users have to jointly designed. In this paper, we consider such a joint design problem for maximizing the max-min-fairness (MMF)...
In this paper, we consider a wireless network with one full-duplex (FD) base station (BS) and a set of half-duplex (HD) user equipments (UEs). In such scenario, in addition to the self-interference, the co-channel interference from uplink UEs to downlink UEs is the main bottleneck for the network performance. To overcome this, we consider the problem of maximizing the minimum fairness rate among all...
In this paper, we consider a distributed load control problem for achieving supply-demand balance and frequency regulation in a power system. While most of the distributed load control schemes require the loads to exchange information through two-way communications, recent results have shown that it is possible to achieve fully distributed control by frequency-based imbalance estimation. However,...
In this paper, we consider a cloud radio access network (CRAN) with full duplex (FD) remote radio heads (RRHs) and half duplex mobile users. Compared with half duplex RRHs, though FD-RRHs can simultaneously transmit and receive data streams, they also suffer from new interference sources such as self-interference and inter-RRH interference. With FDRRHs, the downlink mobile users (DMUs) are also interfered...
The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a popular algorithm based on the alternating direction method of multipliers proposed in Boyd et al 2011. We show that the algorithm converges globally to...
We consider solving a convex, nonsmooth and stochastic optimization problem over a multi-agent network. Each agent has access to a local objective function and can communicate with its immediate neighbors only. We develop a dynamic stochastic proximal-gradient consensus (DySPGC) algorithm, featuring: i) it works for both the static and randomly time-varying networks; ii) it can deal with either the...
Alternating direction method of multipliers (ADMM) has been recognized as an efficient approach for solving many large-scale learning problems over a computer cluster. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. In this paper, we propose an asynchronous distributed ADMM (AD- ADMM) which can...
In handling massive-scale signal processing problems arising from ‘big-data’ applications, key technologies could come from the development of decentralized algorithms. In this context, consensus-based methods have been advocated because of their simplicity, fault tolerance and versatility. This paper presents a new consensus-based decentralized algorithm for a class of non-convex optimization problems...
Recently, the alternating direction method of multipliers (ADMM) has been used for distributed consensus optimization and is shown to converge faster than conventional approaches based on consensus subgradient. In this paper, we consider a convex optimization problem with a linearly coupled equality constraint and employ a dual consensus ADMM (DC-ADMM) method for solving the problem in a fully distributed...
This paper considers an energy-efficient packet scheduling problem over green data networks, aiming at minimizing the transmission energy subject to the First-In-First-Out and strict delay constraints. Traditionally, such a problem is studied based on the classical Shannon capacity formula. However, Shannon capacity is valid only when the code blocklength approaches infinity and therefore is not practical...
In this work, we consider the joint day-ahead power bidding and load scheduling problem for the smart grid system, in the presence of uncertain energy demand and renewable energy generation. We formulate the problem as a convex stochastic program in which the renewable energy generation and energy demand are modeled as random variables. The objective is to minimize the cost in the day-ahead market...
Max-min-fairness (MMF), which concerns optimizing the worst signal-to-interference-plus-noise ratio (SINR) performance of receivers, is a popular transmitter design criterion in multiuser communications. In the single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output (SIMO) interference channels with perfect channel state information at the transmitters,...
The multi-agent distributed consensus optimization problem arises in many engineering applications. Recently, the alternating direction method of multipliers (ADMM) has been applied to distributed consensus optimization which, referred to as the consensus ADMM (C-ADMM), can converge much faster than conventional consensus subgradient methods. However, C-ADMM can be computationally expensive when the...
In this paper, we consider a nonsmooth convex problem with linear coupling constraints. Problems of this form arise in many modern large-scale signal processing applications including the provision of smart grid networks. In this work, we propose a new class of algorithms called the block coordinate descent method of multipliers (BCDMM) to solve this family of problems. The BCDMM is a primal-dual...
Demand side management (DSM) has been one of the enabling technologies for the smart grid systems. This paper considers the coordinated DSM (CoDSM) technique where a load aggregator coordinates the energy consumption of a neighborhood with large numbers of customers, in order to achieve real-time power balance. The deferrable loads (such as the Plug-in (Hybrid) Electric Vehicles (PHEV) and washing...
This paper studies the decentralized solution of a multi-agent sparse regression problem in the form of a globally coupled objective function with a non-smooth sparsity promoting constraint. In particular, we propose a distributed primal-dual perturbation (PDP) method which combines the average consensus technique and the primaldual perturbed subgradient method. Compared to the conventional primal-dual...
A critical task in smart grid is to gain situational awareness by performing state estimation. In this paper, we consider the problem of placing a type of special sensors, called Phasor Measurement Units (PMU), to optimize the performance and convergence of state estimation. We derive a metric to evaluate how the placement impacts the convergence and accuracy of state estimation solved by Gauss-Newton...
This paper considers beamforming designs for weighted sum rate maximization (WSRM) in a multiple-input single-output interference channel subject to probability constraints on the rate outage. We claim that the outage probability constrained WSRM problem is an NP-hard problem, and therefore focus on devising efficient approximation methods. In particular, inspired by an insightful problem reformulation,...
This paper investigates channel coded transmission schemes for two-way relay networks (TWRNs) where two terminal nodes exchange information through a set of amplify-and-forward (AF) relays over frequency-selective fading channels. Specifically, we assume that the two terminal nodes employ bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM) for coded data transmission,...
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