76,899 research outputs found
Energy-Efficient Heterogeneous Cellular Networks with Spectrum Underlay and Overlay Access
In this paper, we provide joint subcarrier assignment and power allocation
schemes for quality-of-service (QoS)-constrained energy-efficiency (EE)
optimization in the downlink of an orthogonal frequency division multiple
access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering
underlay transmission, where spectrum-efficiency (SE) is fully exploited, the
EE solution involves tackling a complex mixed-combinatorial and non-convex
optimization problem. With appropriate decomposition of the original problem
and leveraging on the quasi-concavity of the EE function, we propose a
dual-layer resource allocation approach and provide a complete solution using
difference-of-two-concave-functions approximation, successive convex
approximation, and gradient-search methods. On the other hand, the inherent
inter-tier interference from spectrum underlay access may degrade EE
particularly under dense small-cell deployment and large bandwidth utilization.
We therefore develop a novel resource allocation approach based on the concepts
of spectrum overlay access and resource efficiency (RE) (normalized EE-SE
trade-off). Specifically, the optimization procedure is separated in this case
such that the macro-cell optimal RE and corresponding bandwidth is first
determined, then the EE of small-cells utilizing the remaining spectrum is
maximized. Simulation results confirm the theoretical findings and demonstrate
that the proposed resource allocation schemes can approach the optimal EE with
each strategy being superior under certain system settings
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 Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
Separable Convex Optimization with Nested Lower and Upper Constraints
We study a convex resource allocation problem in which lower and upper bounds
are imposed on partial sums of allocations. This model is linked to a large
range of applications, including production planning, speed optimization,
stratified sampling, support vector machines, portfolio management, and
telecommunications. We propose an efficient gradient-free divide-and-conquer
algorithm, which uses monotonicity arguments to generate valid bounds from the
recursive calls, and eliminate linking constraints based on the information
from sub-problems. This algorithm does not need strict convexity or
differentiability. It produces an -approximate solution for the
continuous problem in time
and an integer solution in time, where is
the number of decision variables, is the number of constraints, and is
the resource bound. A complexity of is also achieved
for the linear and quadratic cases. These are the best complexities known to
date for this important problem class. Our experimental analyses confirm the
good performance of the method, which produces optimal solutions for problems
with up to 1,000,000 variables in a few seconds. Promising applications to the
support vector ordinal regression problem are also investigated
- …