Xiaohan Wei

DOA Estimation Using a Greedy Block Coordinate Descent Algorithm

This paper presents a novel jointly sparse signal reconstruction algorithm for the DOA estimation problem, aiming to achieve faster convergence rate and better estimation accuracy compared to existing l2,1-norm minimization approaches. The proposed greedy block coordinate descent (GBCD) algorithm shares similarity with the standard block coordinate descent method for l2,1-norm minimization, but adopts a greedy block selection rule which gives preference to sparsity. Although greedy, the proposed algorithm is proved to also have global convergence in this paper. Through theoretical analysis we demonstrate its stability in the sense that all nonzero supports found by the proposed algorithm are the actual ones under certain conditions. Last, we move forward to propose a weighted form of the block selection rule based on the MUSIC prior. The refinement greatly improves the estimation accuracy especially when two point sources are closely spaced. Numerical experiments show that the proposed GBCD algorithm has several notable advantages over the existing DOA estimation methods, such as fast convergence rate, accurate reconstruction, and noise resistance.

Power aware wireless file downloading: A constrained restless bandit approach

This paper treats power-aware throughput maximization in a multi-user file downloading system. Each user can receive a new file only after its previous file is finished. The file state processes for each user act as coupled Markov chains that form a generalized restless bandit system. First, an optimal algorithm is derived for the case of one user. The algorithm maximizes throughput subject to an average power constraint. Next, the one-user algorithm is extended to a low complexity heuristic for the multi-user problem. The heuristic uses a simple online index policy and its effectiveness is shown via simulation. For simple 3-user cases where the optimal solution can be computed offline, the heuristic is shown to be near-optimal for a wide range of parameters.

Data Center Server Provision: Distributed Asynchronous Control for Coupled Renewal Systems

This paper considers a cost minimization problem for data centers with

Robust group LASSO over decentralized networks

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Power-Aware Wireless File Downloading: A Lyapunov Indexing Approach to a Constrained Restless Bandit Problem

servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle, and setup) with different costs and time durations. The servers operate asynchronously over their own states and can choose one of multiple sleep modes when idle. We develop an online distributed control algorithm so that each server makes its own decisions. The request queues are bounded and the overall time average cost is near optimal with probability 1. First the algorithm does not need probability information for the arrival rate or job sizes. Finally, an improved algorithm that uses a single queue is developed via a “virtualization” technique, which is shown to provide the same (near optimal) costs. Simulation experiments on a real data center traffic trace demonstrate the efficiency of our algorithm compared with other existing algorithms.

Delay optimal power aware opportunistic scheduling with mutual information accumulation

This paper considers the recovery of group sparse signals over a multi-agent network, where the measurements are subject to sparse errors. We first investigate the robust group LASSO model and its centralized algorithm based on the alternating direction method of multipliers (ADMM), which requires a central fusion center to compute a global row-support detector. To implement it in a decentralized network environment, we then adopt dynamic average consensus strategies that enable dynamic tracking of the global row-support detector. Numerical experiments demonstrate the effectiveness of the proposed algorithms.

Asynchronous Optimization over Weakly Coupled Renewal Systems

This paper treats power-aware throughput maximization in a multiuser file downloading system. Each user can receive a new file only after its previous file is finished. The file state processes for each user act as coupled Markov chains that form a generalized restless bandit system. First, an optimal algorithm is derived for the case of one user. The algorithm maximizes throughput subject to an average power constraint. Next, the one-user algorithm is extended to a low-complexity heuristic for the multiuser problem. The heuristic uses a simple online index policy. In a special case with no power-constraint, the multiuser heuristic is shown to be throughput-optimal. Simulations are used to demonstrate effectiveness of the heuristic in the general case. For simple cases where the optimal solution can be computed offline, the heuristic is shown to be near-optimal for a wide range of parameters.

Online Convex Optimization with Stochastic Constraints

This paper considers optimization of power and delay in a time-varying wireless link using rateless codes. The link serves a sequence of variable-length packets. Each packet is coded and transmitted over multiple slots. Channel conditions can change from slot to slot and are unknown to the transmitter. The amount of mutual information accumulated on each slot depends on the random channel realization and the power used. The goal is to minimize average service delay subject to an average power constraint. We formulate this problem as a frame-based stochastic optimization problem and solve it via an online algorithm. We show that the subproblem within each frame is a simple integer program which can be effectively solved using a dynamic program. The optimality of this online algorithm is proved using the frame-based Lyapunov drift analysis.