2,236 research outputs found
Real-time dynamic spectrum management for multi-user multi-carrier communication systems
Dynamic spectrum management is recognized as a key technique to tackle
interference in multi-user multi-carrier communication systems and networks.
However existing dynamic spectrum management algorithms may not be suitable
when the available computation time and compute power are limited, i.e., when a
very fast responsiveness is required. In this paper, we present a new paradigm,
theory and algorithm for real-time dynamic spectrum management (RT-DSM) under
tight real-time constraints. Specifically, a RT-DSM algorithm can be stopped at
any point in time while guaranteeing a feasible and improved solution. This is
enabled by the introduction of a novel difference-of-variables (DoV)
transformation and problem reformulation, for which a primal coordinate ascent
approach is proposed with exact line search via a logarithmicly scaled grid
search. The concrete proposed algorithm is referred to as iterative power
difference balancing (IPDB). Simulations for different realistic wireline and
wireless interference limited systems demonstrate its good performance, low
complexity and wide applicability under different configurations.Comment: 14 pages, 9 figures. This work has been submitted to the IEEE for
possible publicatio
Distributed Interference-Aware Energy-Efficient Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks
The introduction of device-to-device (D2D) into cellular networks poses many
new challenges in the resource allocation design due to the co-channel
interference caused by spectrum reuse and limited battery life of user
equipments (UEs). In this paper, we propose a distributed interference-aware
energy-efficient resource allocation algorithm to maximize each UE's energy
efficiency (EE) subject to its specific quality of service (QoS) and maximum
transmission power constraints. We model the resource allocation problem as a
noncooperative game, in which each player is self-interested and wants to
maximize its own EE. The formulated EE maximization problem is a non-convex
problem and is transformed into a convex optimization problem by exploiting the
properties of the nonlinear fractional programming. An iterative optimization
algorithm is proposed and verified through computer simulations.Comment: 6 pages, 3 figures, IEEE GLOBECOM 201
Joint Power Allocation and User Association Optimization for Massive MIMO Systems
This paper investigates the joint power allocation and user association
problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink
(DL) systems. The target is to minimize the total transmit power consumption
when each user is served by an optimized subset of the base stations (BSs),
using non-coherent joint transmission. We first derive a lower bound on the
ergodic spectral efficiency (SE), which is applicable for any channel
distribution and precoding scheme. Closed-form expressions are obtained for
Rayleigh fading channels with either maximum ratio transmission (MRT) or zero
forcing (ZF) precoding. From these bounds, we further formulate the DL power
minimization problems with fixed SE constraints for the users. These problems
are proved to be solvable as linear programs, giving the optimal power
allocation and BS-user association with low complexity. Furthermore, we
formulate a max-min fairness problem which maximizes the worst SE among the
users, and we show that it can be solved as a quasi-linear program. Simulations
manifest that the proposed methods provide good SE for the users using less
transmit power than in small-scale systems and the optimal user association can
effectively balance the load between BSs when needed. Even though our framework
allows the joint transmission from multiple BSs, there is an overwhelming
probability that only one BS is associated with each user at the optimal
solution.Comment: 16 pages, 12 figures, Accepted by IEEE Trans. Wireless Commu
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
Green and fast DSL via joint processing of multiple lines and time–frequency packed modulation
In this paper, strategies to enhance the performance, in terms of available data-rate per user, energy efficiency, and spectral efficiency, of current digital subscriber lines (DSL) are proposed. In particular, a system wherein a group of copper wires is jointly processed at both ends of the communication link is considered. For such a scenario, a resource allocation scheme aimed at energy efficiency maximization is proposed, and, moreover, time–frequency packed modulation schemes are investigated for increased spectral efficiency. Results show that a joint processing of even a limited number of wires at both ends of the communication links brings remarkable performance improvements with respect to the case of individual point-to-point DSL connections; moreover, the considered solution does represent a viable means to increase, in the short term, the data-rate of the wired access network, without an intensive (and expensive) deployment of optical links
Energy-Efficient Antenna Selection and Power Allocation for Large-Scale Multiple Antenna Systems with Hybrid Energy Supply
The combination of energy harvesting and large-scale multiple antenna
technologies provides a promising solution for improving the energy efficiency
(EE) by exploiting renewable energy sources and reducing the transmission power
per user and per antenna. However, the introduction of energy harvesting
capabilities into large-scale multiple antenna systems poses many new
challenges for energy-efficient system design due to the intermittent
characteristics of renewable energy sources and limited battery capacity.
Furthermore, the total manufacture cost and the sum power of a large number of
radio frequency (RF) chains can not be ignored, and it would be impractical to
use all the antennas for transmission. In this paper, we propose an
energy-efficient antenna selection and power allocation algorithm to maximize
the EE subject to the constraint of user's quality of service (QoS). An
iterative offline optimization algorithm is proposed to solve the non-convex EE
optimization problem by exploiting the properties of nonlinear fractional
programming. The relationships among maximum EE, selected antenna number,
battery capacity, and EE-SE tradeoff are analyzed and verified through computer
simulations.Comment: IEEE Globecom 2014 Selected Areas in Communications Symposium-Green
Communications and Computing Trac
Modified SNR gap approximation for resource allocation in LDPC-coded multicarrier systems
The signal-to-noise ratio (SNR) gap approximation provides a closed-form expression for the SNR required for a coded modulation system to achieve a given target error performance for a given constellation size. This approximation has been widely used for resource allocation in the context of trellis-coded multicarrier systems (e.g., for digital subscriber line communication). In this contribution, we show that the SNR gap approximation does not accurately model the relation between constellation size and required SNR in low-density parity-check (LDPC) coded multicarrier systems. We solve this problem by using a simple modification of the SNR gap approximation instead, which fully retains the analytical convenience of the former approximation. The performance advantage resulting from the proposed modification is illustrated for single-user digital subscriber line transmission
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