8 research outputs found
Optimal Joint Power and Subcarrier Allocation for MC-NOMA Systems
In this paper, we investigate the resource allocation algorithm design for
multicarrier non-orthogonal multiple access (MC-NOMA) systems. The proposed
algorithm is obtained from the solution of a non-convex optimization problem
for the maximization of the weighted system throughput. We employ monotonic
optimization to develop the optimal joint power and subcarrier allocation
policy. The optimal resource allocation policy serves as a performance
benchmark due to its high complexity. Furthermore, to strike a balance between
computational complexity and optimality, a suboptimal scheme with low
computational complexity is proposed. Our simulation results reveal that the
suboptimal algorithm achieves a close-to-optimal performance and MC-NOMA
employing the proposed resource allocation algorithm provides a substantial
system throughput improvement compared to conventional multicarrier orthogonal
multiple access (MC-OMA).Comment: Submitted to Globecom 201
Sum Rate Maximized Resource Allocation in Multiple DF Relays Aided OFDM Transmission
In relay-aided wireless transmission systems, one of the key issues is how to
decide assisting relays and manage the energy resource at the source and each
individual relay, to maximize a certain objective related to system
performance. This paper addresses the sum rate maximized resource allocation
(RA) problem in a point to point orthogonal frequency division modulation
(OFDM) transmission system assisted by multiple decode-and-forward (DF) relays,
subject to the individual sum power constraints of the source and the relays.
In particular, the transmission at each subcarrier can be in either the direct
mode without any relay assisting, or the relay-aided mode with one or several
relays assisting. We propose two RA algorithms which optimize the assignment of
transmission mode and source power for every subcarrier, as well as the
assisting relays and the power allocation to them for every {relay-aided}
subcarrier. First, it is shown that the considered RA problem has zero
Lagrangian duality gap when there is a big number of subcarriers. In this case,
a duality based algorithm that finds a globally optimum RA is developed.
Second, a coordinate-ascent based iterative algorithm, which finds a suboptimum
RA but is always applicable regardless of the duality gap of the RA problem, is
developed. The effectiveness of these algorithms has been illustrated by
numerical experiments.Comment: 13 pages in two-column format, 10 figures, to appear in IEEE Journal
on Selected Areas in Communication
Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks
This paper presents a distributed resource allocation algorithm to jointly
optimize the power allocation, channel allocation and relay selection for
decode-and-forward (DF) relay networks with a large number of sources, relays,
and destinations. The well-known dual decomposition technique cannot directly
be applied to resolve this problem, because the achievable data rate of DF
relaying is not strictly concave, and thus the local resource allocation
subproblem may have non-unique solutions. We resolve this non-strict concavity
problem by using the idea of the proximal point method, which adds quadratic
terms to make the objective function strictly concave. However, the proximal
solution adds an extra layer of iterations over typical duality based
approaches, which can significantly slow down the speed of convergence. To
address this key weakness, we devise a fast algorithm without the need for this
additional layer of iterations, which converges to the optimal solution. Our
algorithm only needs local information exchange, and can easily adapt to
variations of network size and topology. We prove that our distributed resource
allocation algorithm converges to the optimal solution. A channel resource
adjustment method is further developed to provide more channel resources to the
bottleneck links and realize traffic load balance. Numerical results are
provided to illustrate the benefits of our algorithm