12,350 research outputs found
Distributed Algorithms for Spectrum Allocation, Power Control, Routing, and Congestion Control in Wireless Networks
We develop distributed algorithms to allocate resources in multi-hop wireless
networks with the aim of minimizing total cost. In order to observe the
fundamental duplexing constraint that co-located transmitters and receivers
cannot operate simultaneously on the same frequency band, we first devise a
spectrum allocation scheme that divides the whole spectrum into multiple
sub-bands and activates conflict-free links on each sub-band. We show that the
minimum number of required sub-bands grows asymptotically at a logarithmic rate
with the chromatic number of network connectivity graph. A simple distributed
and asynchronous algorithm is developed to feasibly activate links on the
available sub-bands. Given a feasible spectrum allocation, we then design
node-based distributed algorithms for optimally controlling the transmission
powers on active links for each sub-band, jointly with traffic routes and user
input rates in response to channel states and traffic demands. We show that
under specified conditions, the algorithms asymptotically converge to the
optimal operating point.Comment: 14 pages, 5 figures, submitted to IEEE/ACM Transactions on Networkin
Joint User-Association and Resource-Allocation in Virtualized Wireless Networks
In this paper, we consider a down-link transmission of multicell virtualized
wireless networks (VWNs) where users of different service providers (slices)
within a specific region are served by a set of base stations (BSs) through
orthogonal frequency division multiple access (OFDMA). In particular, we
develop a joint BS assignment, sub-carrier and power allocation algorithm to
maximize the network throughput, while satisfying the minimum required rate of
each slice. Under the assumption that each user at each transmission instance
can connect to no more than one BS, we introduce the user-association factor
(UAF) to represent the joint sub-carrier and BS assignment as the optimization
variable vector in the mathematical problem formulation. Sub-carrier reuse is
allowed in different cells, but not within one cell. As the proposed
optimization problem is inherently non-convex and NP-hard, by applying the
successive convex approximation (SCA) and complementary geometric programming
(CGP), we develop an efficient two-step iterative approach with low
computational complexity to solve the proposed problem. For a given
power-allocation, Step 1 derives the optimum userassociation and subsequently,
for an obtained user-association, Step 2 find the optimum power-allocation.
Simulation results demonstrate that the proposed iterative algorithm
outperforms the traditional approach in which each user is assigned to the BS
with the largest average value of signal strength, and then, joint sub-carrier
and power allocation is obtained for the assigned users of each cell.
Especially, for the cell-edge users, simulation results reveal a coverage
improvement up to 57% and 71% for uniform and non-uniform users distribution,
respectively leading to more reliable transmission and higher spectrum
efficiency for VWN
Spectrum optimization in multi-user multi-carrier systems with iterative convex and nonconvex approximation methods
Several practical multi-user multi-carrier communication systems are
characterized by a multi-carrier interference channel system model where the
interference is treated as noise. For these systems, spectrum optimization is a
promising means to mitigate interference. This however corresponds to a
challenging nonconvex optimization problem. Existing iterative convex
approximation (ICA) methods consist in solving a series of improving convex
approximations and are typically implemented in a per-user iterative approach.
However they do not take this typical iterative implementation into account in
their design. This paper proposes a novel class of iterative approximation
methods that focuses explicitly on the per-user iterative implementation, which
allows to relax the problem significantly, dropping joint convexity and even
convexity requirements for the approximations. A systematic design framework is
proposed to construct instances of this novel class, where several new
iterative approximation methods are developed with improved per-user convex and
nonconvex approximations that are both tighter and simpler to solve (in
closed-form). As a result, these novel methods display a much faster
convergence speed and require a significantly lower computational cost.
Furthermore, a majority of the proposed methods can tackle the issue of getting
stuck in bad locally optimal solutions, and hence improve solution quality
compared to existing ICA methods.Comment: 33 pages, 7 figures. This work has been submitted for possible
publicatio
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