549 research outputs found
Joint Resource Optimization for Multicell Networks with Wireless Energy Harvesting Relays
This paper first considers a multicell network deployment where the base
station (BS) of each cell communicates with its cell-edge user with the
assistance of an amplify-and-forward (AF) relay node. Equipped with a power
splitter and a wireless energy harvester, the self-sustaining relay scavenges
radio frequency (RF) energy from the received signals to process and forward
the information. Our aim is to develop a resource allocation scheme that
jointly optimizes (i) BS transmit powers, (ii) received power splitting factors
for energy harvesting and information processing at the relays, and (iii) relay
transmit powers. In the face of strong intercell interference and limited radio
resources, we formulate three highly-nonconvex problems with the objectives of
sum-rate maximization, max-min throughput fairness and sum-power minimization.
To solve such challenging problems, we propose to apply the successive convex
approximation (SCA) approach and devise iterative algorithms based on geometric
programming and difference-of-convex-functions programming. The proposed
algorithms transform the nonconvex problems into a sequence of convex problems,
each of which is solved very efficiently by the interior-point method. We prove
that our algorithms converge to the locally optimal solutions that satisfy the
Karush-Kuhn-Tucker conditions of the original nonconvex problems. We then
extend our results to the case of decode-and-forward (DF) relaying with
variable timeslot durations. We show that our resource allocation solutions in
this case offer better throughput than that of the AF counterpart with equal
timeslot durations, albeit at a higher computational complexity. Numerical
results confirm that the proposed joint optimization solutions substantially
improve the network performance, compared with cases where the radio resource
parameters are individually optimized
Energy-Efficient Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
The concept of Smart Cities has been introduced as a way to benefit from the
digitization of various ecosystems at a city level. To support this concept,
future communication networks need to be carefully designed with respect to the
city infrastructure and utilization of resources. Recently, the idea of 'smart'
environment, which takes advantage of the infrastructure for better performance
of wireless networks, has been proposed. This idea is aligned with the recent
advances in design of reconfigurable intelligent surfaces (RISs), which are
planar structures with the capability to reflect impinging electromagnetic
waves toward preferred directions. Thus, RISs are expected to provide the
necessary flexibility for the design of the 'smart' communication environment,
which can be optimally shaped to enable cost- and energy-efficient signal
transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart
Cities would become even more controllable and adaptable, which would
subsequently ease the implementation of future communication networks in urban
areas and boost the interconnection among private households and public
services. In this paper, we describe our vision of the application of RISs in
future Smart Cities. In particular, the research challenges and opportunities
are addressed. The contribution paves the road to a systematic design of
RIS-assisted communication networks for Smart Cities in the years to come.Comment: Submitted for possible publication in IEEE Open Journal of the
Communications Societ
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