Decentralized Consensus Optimization Based on Parallel Random Walk

IEEE Wireless Commun. Lett.

et al. 2021

Self Cite

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) is required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.Index Terms-Beamforming, cell-free networks, intelligent reflecting surface, decentralized optimization. I. INTRODUCTIONRecently, a user-centric network paradigm called cellfree networks has been considered as a promising technique to provide high network capacity and overcome the cellboundary effect of traditional network-centric networks (e.g., cellular networks) [1]-[4]. In cell-free networks, a large number of distributed service antennas, which are connected to central processing units (CPUs), coherently serve all users on the same time-frequency resource [2]. This distributed communication network can offer many degrees of freedom and high multiplexing gain. Recent results show that cellfree networks outperform traditional cellular and small-cell networks in several practical scenarios [2], [3]. To provide high directional gains, beamforming design is important in cell-free networks. To cooperatively design beamforming, a centralized zero-forcing (ZF) beamforming scheme is proposed in [5]. Since the CPU should collect all instantaneous channel state information (CSI) of all base stations (BSs), centralized approaches might be unsalable when the number of BSs and users (UEs) is large and the beamforming optimization at the CPU may be overwhelming due to the high dimensionality of aggregated beamformers. To avoid instantaneous CSI exchange among BSs via backhauling and reduce

Section: Decentralized Beamforming Designmentioning

confidence: 99%

Section: Decentralized Beamforming Designmentioning

confidence: 99%

Decentralized Beamforming Design for Intelligent Reflecting Surface-Enhanced Cell-Free Networks

Huang

IEEE Wireless Commun. Lett.

et al. 2021

Self Cite

“…Furthermore, an incremental learning method has also been recognized as a promising approach to reduce communication costs, which activates one agent and one link at each iteration in a cyclic or a random order whilst keeping all other agents and links idle. Randomwalk ADMM (WADMM) [5], parallel random walk ADMM (PW-ADMM) [6], and walk proximal gradient (WPG) [7] are commonly used incremental methods. Specifically, the sequence of the updating order for agents is randomized by following a Markov chain in WADMM, while PW-ADMM allows multiple random walks in parallel.…”

mentioning

confidence: 99%

Coded Stochastic ADMM for Decentralized Consensus Optimization With Edge Computing

Chen

IEEE Internet Things J.

et al. 2021

Self Cite

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones, and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to analyze information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multiagent system with data locally processed via distributed edge nodes. A class of minibatch stochastic alternating direction method of multipliers (ADMMs) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed learning systems, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding-based stochastic incremental ADMM is investigated. Given an appropriate minibatch size, we show that the minibatch stochastic ADMM-based method converges in a rate of O(1/ √ k), where k denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication efficient, rapidly responding, and robust in the presence of straggler nodes compared with state-of-the-art algorithms.

“…In the existing literature, e.g., [11]- [18], a large number of decentralized algorithms have been investigated to solve the consensus problem (1). Typically, the algorithms can mainly be classified into primal and primal-dual types, namely, gradient descent based (GD) methods and the alternating direction method of multipliers (ADMM) based methods, respectively.…”

mentioning

confidence: 99%

“…Typically, the algorithms can mainly be classified into primal and primal-dual types, namely, gradient descent based (GD) methods and the alternating direction method of multipliers (ADMM) based methods, respectively. In this work, we will use ADMM as the optimizer, which can usually achieve more accurate consensus performance than GD based methods with constant step size [18].…”

mentioning

confidence: 99%

Privacy-Preserving Incremental ADMM for Decentralized Consensus Optimization

Chen

IEEE Trans. Signal Process.

et al. 2020

Self Cite

The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm, the updating order of which follows a Hamiltonian cycle instead. However, I-ADMM cannot guarantee the privacy for agents against external eavesdroppers even if the randomized initialization is applied. To protect privacy for agents, we then propose two privacy-preserving incremental ADMM algorithms, i.e., PI-ADMM1 and PI-ADMM2, where perturbation over step sizes and primal variables is adopted, respectively. Through theoretical analyses, we prove the convergence and privacy preservation for PI-ADMM1, which are further supported by numerical experiments. Besides, simulations demonstrate that the proposed PI-ADMM1 and PI-ADMM2 algorithms are communication efficient compared with state-of-the-art methods.