7,211 research outputs found
Hybrid analog-digital transmit beamforming for spectrum sharing backhaul networks
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper deals with the problem of analog-digital transmit beamforming under spectrum sharing constraints for backhaul systems. In contrast to fully digital designs, where the spatial processing is done at baseband unit with all the flexible computational resources of digital processors, analog-digital beamforming schemes require that certain processing is done through analog components, such as phase-shifters or switches. These analog components do not have the same processing flexibility as the digital processor, but on the other hand, they can substantially reduce the cost and complexity of the beamforming solution. This paper presents the joint optimization of the analog and digital parts, which results in a nonconvex, NP-hard, and coupled problem. In order to solve it, an alternating optimization with a penalized convex-concave method is proposed. According to the simulation results, this novel iterative procedure is able to find a solution that behaves close to the fully digital beamforming upper bound scheme.Peer ReviewedPostprint (author's final draft
Spectrum optimization in multi-user multi-carrier systems with iterative convex and nonconvex approximation methods
Several practical multi-user multi-carrier communication systems are
characterized by a multi-carrier interference channel system model where the
interference is treated as noise. For these systems, spectrum optimization is a
promising means to mitigate interference. This however corresponds to a
challenging nonconvex optimization problem. Existing iterative convex
approximation (ICA) methods consist in solving a series of improving convex
approximations and are typically implemented in a per-user iterative approach.
However they do not take this typical iterative implementation into account in
their design. This paper proposes a novel class of iterative approximation
methods that focuses explicitly on the per-user iterative implementation, which
allows to relax the problem significantly, dropping joint convexity and even
convexity requirements for the approximations. A systematic design framework is
proposed to construct instances of this novel class, where several new
iterative approximation methods are developed with improved per-user convex and
nonconvex approximations that are both tighter and simpler to solve (in
closed-form). As a result, these novel methods display a much faster
convergence speed and require a significantly lower computational cost.
Furthermore, a majority of the proposed methods can tackle the issue of getting
stuck in bad locally optimal solutions, and hence improve solution quality
compared to existing ICA methods.Comment: 33 pages, 7 figures. This work has been submitted for possible
publicatio
Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks
The cloud-radio access network (CRAN) is expected to be the core network
architecture for next generation mobile radio systems. In this paper, we
consider the downlink of a CRAN formed of one central processor (the cloud) and
several base-station (BS), where each BS is connected to the cloud via either a
wireless or capacity-limited wireline backhaul link. The paper addresses the
joint design of the hybrid backhaul links (i.e., designing the wireline and
wireless backhaul connections from the cloud to the BSs) and the access links
(i.e., determining the sparse beamforming solution from the BSs to the users).
The paper formulates the hybrid backhaul and access link design problem by
minimizing the total network power consumption. The paper solves the problem
using a two-stage heuristic algorithm. At one stage, the sparse beamforming
solution is found using a weighted mixed `1=`2 norm minimization approach; the
correlation matrix of the quantization noise of the wireline backhaul links is
computed using the classical rate-distortion theory. At the second stage, the
transmit powers of the wireless backhaul links are found by solving a power
minimization problem subject to quality-of-service constraints, based on the
principle of conservation of rate by utilizing the rates found in the first
stage. Simulation results suggest that the performance of the proposed
algorithm approaches the global optimum solution, especially at high
signal-to-interference-plus-noise ratio (SINR).Comment: 6 pages, 3 figures, IWCPM 201
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