54 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
Performance Analysis of Cooperative V2V and V2I Communications under Correlated Fading
Cooperative vehicular networks will play a vital role in the coming years to
implement various intelligent transportation-related applications. Both
vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications
will be needed to reliably disseminate information in a vehicular network. In
this regard, a roadside unit (RSU) equipped with multiple antennas can improve
the network capacity. While the traditional approaches assume antennas to
experience independent fading, we consider a more practical uplink scenario
where antennas at the RSU experience correlated fading. In particular, we
evaluate the packet error probability for two renowned antenna correlation
models, i.e., constant correlation (CC) and exponential correlation (EC). We
also consider intermediate cooperative vehicles for reliable communication
between the source vehicle and the RSU. Here, we derive closed-form expressions
for packet error probability which help quantify the performance variations due
to fading parameter, correlation coefficients and the number of intermediate
helper vehicles. To evaluate the optimal transmit power in this network
scenario, we formulate a Stackelberg game, wherein, the source vehicle is
treated as a buyer and the helper vehicles are the sellers. The optimal
solutions for the asking price and the transmit power are devised which
maximize the utility functions of helper vehicles and the source vehicle,
respectively. We verify our mathematical derivations by extensive simulations
in MATLAB.Comment: Internet of Vehicles (IoV), Vehicular communication, Antenna
correlation, Stackelberg game, Vehicle-to-infrastructure (V2I),
Vehicle-to-vehicle (V2V), Game theory, Cooperative vehicular network
Energy-Efficient Design for Downlink Cloud Radio Access Networks
This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.This work is supported in part by an ECR-HDR scholarship
from The University of Newcastle, in part by the Australian
Research Council Discovery Project grants DP170100939 and
DP160101537, in part by Vietnam National Foundation for
Science and Technology Development under grant number
101.02-2016.11 and in part by a startup fund from San Diego
State University
User Selection Approaches to Mitigate the Straggler Effect for Federated Learning on Cell-Free Massive MIMO Networks
This work proposes UE selection approaches to mitigate the straggler effect
for federated learning (FL) on cell-free massive multiple-input multiple-output
networks. To show how these approaches work, we consider a general FL framework
with UE sampling, and aim to minimize the FL training time in this framework.
Here, training updates are (S1) broadcast to all the selected UEs from a
central server, (S2) computed at the UEs sampled from the selected UE set, and
(S3) sent back to the central server. The first approach mitigates the
straggler effect in both Steps (S1) and (S3), while the second approach only
Step (S3). Two optimization problems are then formulated to jointly optimize UE
selection, transmit power and data rate. These mixed-integer mixed-timescale
stochastic nonconvex problems capture the complex interactions among the
training time, the straggler effect, and UE selection. By employing the online
successive convex approximation approach, we develop a novel algorithm to solve
the formulated problems with proven convergence to the neighbourhood of their
stationary points. Numerical results confirm that our UE selection designs
significantly reduce the training time over baseline approaches, especially in
the networks that experience serious straggler effects due to the moderately
low density of access points.Comment: submitted for peer review
Spectral and Energy Efficiency Maximization for Content-Centric C-RANs with Edge Caching
This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group's user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies.This work is supported in part by an ECRHDR scholarship from The University of Newcastle, in part by the Australian Research Council Discovery Project grants DP170100939 and DP160101537
Sum throughput maximization for heterogeneous multicell networks with RF-powered relays
This paper considers a heterogeneous multicell network
where the base station (BS) in each cell communicates with
its cell-edge user with the assistance of an amplify-and-forward
relay node. Equipped with a power splitter and a wireless energy
harvester, the relay scavenges RF energy from the received signals to
process and forward the information. In the face of strong intercell
interference and limited radio resources, we develop a resource
allocation scheme that jointly optimizes (i) BS transmit powers,
(ii) power splitting factors for energy harvesting and information
processing at the relays, and (iii) relay transmit powers. To solve the
highly non-convex problem formulation of sum-rate maximization,
we propose to apply the successive convex approximation (SCA)
approach and devise an iterative algorithm based on geometric
programming. The proposed algorithm transforms the nonconvex
problem into a sequence of convex problems, each of which is solved
very efficiently by the interior-point method. We prove that our
developed algorithm converges to an optimal solution that satisfies
the Karush-Kuhn-Tucker conditions of the original nonconvex
problem. Numerical results confirm that our joint optimization
solution substantially improves the network performance, compared
to the existing solution wherein only the received power splitting
factors at the relays are optimizedARC Discovery Projects Grant DP14010113
Free vibration analysis of laminated composite plates based on FSDT using one-dimensional IRBFN method
This paper presents a new effective radial basis function (RBF) collocation technique for the free vibration
analysis of laminated composite plates using the first order shear deformation theory (FSDT). The plates, which can be rectangular or non-rectangular, are simply discretised by means of Cartesian grids. Instead of using conventional differentiated RBF networks, one-dimensional integrated RBF networks (1D-IRBFN) are employed on grid lines to approximate the field variables. A number of examples concerning various thickness-to-span ratios, material properties and boundary conditions are considered. Results obtained are compared with the exact solutions and numerical results by other techniques in the literature to
investigate the performance of the proposed method
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