1,948 research outputs found
Resource Allocation for Outdoor-to-Indoor Multicarrier Transmission with Shared UE-side Distributed Antenna Systems
In this paper, we study the resource allocation algorithm design for downlink
multicarrier transmission with a shared user equipment (UE)-side distributed
antenna system (SUDAS) which utilizes both licensed and unlicensed frequency
bands for improving the system throughput. The joint UE selection and
transceiver processing matrix design is formulated as a non-convex optimization
problem for the maximization of the end-to-end system throughput (bits/s). In
order to obtain a tractable resource allocation algorithm, we first show that
the optimal transmitter precoding and receiver post-processing matrices jointly
diagonalize the end-to-end communication channel. Subsequently, the
optimization problem is converted to a scalar optimization problem for multiple
parallel channels, which is solved by using an asymptotically optimal iterative
algorithm. Simulation results illustrate that the proposed resource allocation
algorithm for the SUDAS achieves an excellent system performance and provides a
spatial multiplexing gain for single-antenna UEs.Comment: accepted for publication at the IEEE Vehicular Technology Conference
(VTC) Spring, Glasgow, Scotland, UK, May 201
Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation
In this paper, we consider the distributive queue-aware power and subband
allocation design for a delay-optimal OFDMA uplink system with one base
station, users and independent subbands. Each mobile has an uplink
queue with heterogeneous packet arrivals and delay requirements. We model the
problem as an infinite horizon average reward Markov Decision Problem (MDP)
where the control actions are functions of the instantaneous Channel State
Information (CSI) as well as the joint Queue State Information (QSI). To
address the distributive requirement and the issue of exponential memory
requirement and computational complexity, we approximate the subband allocation
Q-factor by the sum of the per-user subband allocation Q-factor and derive a
distributive online stochastic learning algorithm to estimate the per-user
Q-factor and the Lagrange multipliers (LM) simultaneously and determine the
control actions using an auction mechanism. We show that under the proposed
auction mechanism, the distributive online learning converges almost surely
(with probability 1). For illustration, we apply the proposed distributive
stochastic learning framework to an application example with exponential packet
size distribution. We show that the delay-optimal power control has the {\em
multi-level water-filling} structure where the CSI determines the instantaneous
power allocation and the QSI determines the water-level. The proposed algorithm
has linear signaling overhead and computational complexity ,
which is desirable from an implementation perspective.Comment: To appear in Transactions on Signal Processin
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
Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks with Base Station Coordination
This paper addresses the problem of energy-efficient resource allocation in
the downlink of a cellular OFDMA system. Three definitions of the energy
efficiency are considered for system design, accounting for both the radiated
and the circuit power. User scheduling and power allocation are optimized
across a cluster of coordinated base stations with a constraint on the maximum
transmit power (either per subcarrier or per base station). The asymptotic
noise-limited regime is discussed as a special case. %The performance of both
an isolated and a non-isolated cluster of coordinated base stations is examined
in the numerical experiments. Results show that the maximization of the energy
efficiency is approximately equivalent to the maximization of the spectral
efficiency for small values of the maximum transmit power, while there is a
wide range of values of the maximum transmit power for which a moderate
reduction of the data rate provides a large saving in terms of dissipated
energy. Also, the performance gap among the considered resource allocation
strategies reduces as the out-of-cluster interference increases.Comment: to appear on IEEE Transactions on Wireless Communication
Power adjustment and scheduling in OFDMA femtocell networks
Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%
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