58,954 research outputs found
Distributive Network Utility Maximization (NUM) over Time-Varying Fading Channels
Distributed network utility maximization (NUM) has received an increasing
intensity of interest over the past few years. Distributed solutions (e.g., the
primal-dual gradient method) have been intensively investigated under fading
channels. As such distributed solutions involve iterative updating and explicit
message passing, it is unrealistic to assume that the wireless channel remains
unchanged during the iterations. Unfortunately, the behavior of those
distributed solutions under time-varying channels is in general unknown. In
this paper, we shall investigate the convergence behavior and tracking errors
of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic
scaling matrices (DSC) for solving distributive NUM problems under time-varying
fading channels. We shall also study a specific application example, namely the
multi-commodity flow control and multi-carrier power allocation problem in
multi-hop ad hoc networks. Our analysis shows that the PDSGA converges to a
limit region rather than a single point under the finite state Markov chain
(FSMC) fading channels. We also show that the order of growth of the tracking
errors is given by O(T/N), where T and N are the update interval and the
average sojourn time of the FSMC, respectively. Based on this analysis, we
derive a low complexity distributive adaptation algorithm for determining the
adaptive scaling matrices, which can be implemented distributively at each
transmitter. The numerical results show the superior performance of the
proposed dynamic scaling matrix algorithm over several baseline schemes, such
as the regular primal-dual gradient algorithm
Joint Power Control and Fronthaul Rate Allocation for Throughput Maximization in OFDMA-based Cloud Radio Access Network
The performance of cloud radio access network (C-RAN) is constrained by the
limited fronthaul link capacity under future heavy data traffic. To tackle this
problem, extensive efforts have been devoted to design efficient signal
quantization/compression techniques in the fronthaul to maximize the network
throughput. However, most of the previous results are based on
information-theoretical quantization methods, which are hard to implement due
to the extremely high complexity. In this paper, we consider using practical
uniform scalar quantization in the uplink communication of an orthogonal
frequency division multiple access (OFDMA) based C-RAN system, where the mobile
users are assigned with orthogonal sub-carriers for multiple access. In
particular, we consider joint wireless power control and fronthaul quantization
design over the sub-carriers to maximize the system end-to-end throughput.
Efficient algorithms are proposed to solve the joint optimization problem when
either information-theoretical or practical fronthaul quantization method is
applied. Interestingly, we find that the fronthaul capacity constraints have
significant impact to the optimal wireless power control policy. As a result,
the joint optimization shows significant performance gain compared with either
optimizing wireless power control or fronthaul quantization alone. Besides, we
also show that the proposed simple uniform quantization scheme performs very
close to the throughput performance upper bound, and in fact overlaps with the
upper bound when the fronthaul capacity is sufficiently large. Overall, our
results would help reveal practically achievable throughput performance of
C-RAN, and lead to more efficient deployment of C-RAN in the next-generation
wireless communication systems.Comment: submitted for possible publicatio
- …