Decentralized Minimum Power Multi-Cell Beamforming with Limited Backhaul Signaling

The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

show abstract

“…The former problem is considered in [204,257,265] under total power minimization, while the latter is considered in [14,239,257].…”

Section: Distributed Implementation Of Convex Optimizationmentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

309

317

View full text Add to dashboard Cite

show abstract

“…This section proposes a decentralization solution to the total network power minimization problem (4), using a dual decomposition approach, first proposed in the context of multicell systems [5]. Specifically, we first start by introducing intercloud interference terms, and additional auxiliary variables so as to decouple the SINR constraints.…”

Section: A Proposed Decentralized Solutionmentioning

confidence: 99%

“…Note that the SINR constraints in problem (6) are still coupled across the different clouds, because of the inter-cloud interference terms, which still prevents the derivation of a decentralized strategy. However, using a similar approach to the decoupling strategy, first proposed in the multicell problem [5], (6) can be decoupled by introducing a set of auxiliary local variables at each cloud and a set of corresponding equality constraints. Problem (6) can then be reformulated as:…”

Section: ) Problem Relaxationmentioning

confidence: 99%

“…Both references [2] and [3], however, assume a single-cloud scenario and do not account for inter-cloud interference. The problem in this paper is further related to the multicell beamforming problem studied in conventional cellular networks literature, e.g., [4] [5], which propose decentralized algorithms to solve the problem. The studies in conventional cellular networks [4] [5], however, assume that one user can be served by one BS only, unlike the CRAN architecture which is a user-centric architecture and allows each user to be served by one or more BSs at the same time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized Group Sparse Beamforming for Multi-Cloud Radio Access Networks

Dhifallah

Dahrouj

Al-Naffouri

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

Abstract-Recent studies on cloud-radio access networks (CRANs) assume the availability of a single processor (cloud) capable of managing the entire network performance; inter-cloud interference is treated as background noise. This paper considers the more practical scenario of the downlink of a CRAN formed by multiple clouds, where each cloud is connected to a cluster of multiple-antenna base stations (BSs) via high-capacity wireline backhaul links. The network is composed of several disjoint BSs' clusters, each serving a pre-known set of single-antenna users. To account for both inter-cloud and intra-cloud interference, the paper considers the problem of minimizing the total network power consumption subject to quality of service constraints, by jointly determining the set of active BSs connected to each cloud and the beamforming vectors of every user across the network. The paper solves the problem using Lagrangian duality theory through a dual decomposition approach, which decouples the problem into multiple and independent subproblems, the solution of which depends on the dual optimization problem. The solution then proceeds in updating the dual variables and the active set of BSs at each cloud iteratively. The proposed approach leads to a distributed implementation across the multiple clouds through a reasonable exchange of information between adjacent clouds. The paper further proposes a centralized solution to the problem. Simulation results suggest that the proposed algorithms significantly outperform the conventional per-cloud update solution, especially at high signal-to-interference-plus-noise ratio (SINR) target.

show abstract

“…This implies that the Lagrange multipliers in iteration n + 1 are achieved as lk between base stations that share the lth power constraint. The backhaul signaling load is quantified in [265], where several variations are discussed.…”

Section: Distributed Implementation Of Convex Optimizationmentioning

confidence: 99%