4,446 research outputs found
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink
multi-user communication from a multi-antenna base station is investigated in
this paper. We develop energy-efficient designs for both the transmit power
allocation and the phase shifts of the surface reflecting elements, subject to
individual link budget guarantees for the mobile users. This leads to
non-convex design optimization problems for which to tackle we propose two
computationally affordable approaches, capitalizing on alternating
maximization, gradient descent search, and sequential fractional programming.
Specifically, one algorithm employs gradient descent for obtaining the RIS
phase coefficients, and fractional programming for optimal transmit power
allocation. Instead, the second algorithm employs sequential fractional
programming for the optimization of the RIS phase shifts. In addition, a
realistic power consumption model for RIS-based systems is presented, and the
performance of the proposed methods is analyzed in a realistic outdoor
environment. In particular, our results show that the proposed RIS-based
resource allocation methods are able to provide up to higher energy
efficiency, in comparison with the use of regular multi-antenna
amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included
in the 2018 conference publications at ICASSP
(https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018
(arXiv:1809.05397
Massive MIMO and Small Cells: Improving Energy Efficiency by Optimal Soft-Cell Coordination
To improve the cellular energy efficiency, without sacrificing
quality-of-service (QoS) at the users, the network topology must be densified
to enable higher spatial reuse. We analyze a combination of two densification
approaches, namely "massive" multiple-input multiple-output (MIMO) base
stations and small-cell access points. If the latter are operator-deployed, a
spatial soft-cell approach can be taken where the multiple transmitters serve
the users by joint non-coherent multiflow beamforming. We minimize the total
power consumption (both dynamic emitted power and static hardware power) while
satisfying QoS constraints. This problem is proved to have a hidden convexity
that enables efficient solution algorithms. Interestingly, the optimal solution
promotes exclusive assignment of users to transmitters. Furthermore, we provide
promising simulation results showing how the total power consumption can be
greatly improved by combining massive MIMO and small cells; this is possible
with both optimal and low-complexity beamforming.Comment: Published at International Conference on Telecommunications (ICT
2013), 6-8 May 2013, Casablanca, Morocco, 5 pages, 4 figures, 2 tables. This
version includes the Matlab code necessary to reproduce the simulations; see
the ancillary files. This version also corrects errors in Table 1 and in the
simulations, which affected Figs. 3-
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
- …