184 research outputs found
Maximizing Energy-Efficiency in Multi-Relay OFDMA Cellular Networks
This contribution presents a method of obtaining the optimal power and
subcarrier allocations that maximize the energy-efficiency (EE) of a
multi-user, multi-relay, orthogonal frequency division multiple access (OFDMA)
cellular network. Initially, the objective function (OF) is formulated as the
ratio of the spectral-efficiency (SE) over the power consumption of the
network. This OF is shown to be quasi-concave, thus Dinkelbach's method can be
employed for solving it as a series of parameterized concave problems. We
characterize the performance of the aforementioned method by comparing the
optimal solutions obtained to those found using an exhaustive search.
Additionally, we explore the relationship between the achievable SE and EE in
the cellular network upon increasing the number of active users. In general,
increasing the number of users supported by the system benefits both the SE and
EE, and higher SE values may be obtained at the cost of EE, when an increased
power may be allocated.Comment: 6 pages, 5 figures, 1 table, to appear in Proc. IEEE 2013 56th Global
Communications Conference (GLOBECOM 2013), Atlanta, USA, December, 201
Resource allocation for OFDMA systems with multi-cell joint transmission
This paper considers the downlink resource allocation of a coordinated multi-cell cluster in OFDMA systems with universal frequency reuse. Multi-cell joint transmission is considered via zero-forcing precoding. Furthermore, joint optimization of the user selection and power allocation across multiple subchannels and multiple cells is studied. The objective is to maximize the weighted sum rate under per-base-station power constraints. Based on general duality theory, two iterative resource allocation algorithms are proposed and compared with the optimal solution, which requires an exhaustive search of all possible combinations of users over all subchannels. Simulation results show that the two proposed algorithms achieve a performance very close to the optimal, with much lower computational complexity. In addition, we show that joint user set selection across multiple subchannels significantly improves the system performance in terms of the weighted sum rate
Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility Maximization under Imperfect Channel-State Information
We consider the problem of simultaneous user-scheduling, power-allocation,
and rate-selection in an OFDMA downlink, with the goal of maximizing expected
sum-utility under a sum-power constraint. In doing so, we consider a family of
generic goodput-based utilities that facilitate, e.g., throughput-based
pricing, quality-of-service enforcement, and/or the treatment of practical
modulation-and-coding schemes (MCS). Since perfect knowledge of channel state
information (CSI) may be difficult to maintain at the base-station, especially
when the number of users and/or subchannels is large, we consider scheduling
and resource allocation under imperfect CSI, where the channel state is
described by a generic probability distribution. First, we consider the
"continuous" case where multiple users and/or code rates can time-share a
single OFDMA subchannel and time slot. This yields a non-convex optimization
problem that we convert into a convex optimization problem and solve exactly
using a dual optimization approach. Second, we consider the "discrete" case
where only a single user and code rate is allowed per OFDMA subchannel per time
slot. For the mixed-integer optimization problem that arises, we discuss the
connections it has with the continuous case and show that it can solved exactly
in some situations. For the other situations, we present a bound on the
optimality gap. For both cases, we provide algorithmic implementations of the
obtained solution. Finally, we study, numerically, the performance of the
proposed algorithms under various degrees of CSI uncertainty, utilities, and
OFDMA system configurations. In addition, we demonstrate advantages relative to
existing state-of-the-art algorithms
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