10,001 research outputs found
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
Optimal channel allocation with dynamic power control in cellular networks
Techniques for channel allocation in cellular networks have been an area of
intense research interest for many years. An efficient channel allocation
scheme can significantly reduce call-blocking and calldropping probabilities.
Another important issue is to effectively manage the power requirements for
communication. An efficient power control strategy leads to reduced power
consumption and improved signal quality. In this paper, we present a novel
integer linear program (ILP) formulation that jointly optimizes channel
allocation and power control for incoming calls, based on the
carrier-to-interference ratio (CIR). In our approach we use a hybrid channel
assignment scheme, where an incoming call is admitted only if a suitable
channel is found such that the CIR of all ongoing calls on that channel, as
well as that of the new call, will be above a specified value. Our formulation
also guarantees that the overall power requirement for the selected channel
will be minimized as much as possible and that no ongoing calls will be dropped
as a result of admitting the new call. We have run simulations on a benchmark
49 cell environment with 70 channels to investigate the effect of different
parameters such as the desired CIR. The results indicate that our approach
leads to significant improvements over existing techniques.Comment: 11 page
Bio-Inspired Resource Allocation for Relay-Aided Device-to-Device Communications
The Device-to-Device (D2D) communication principle is a key enabler of direct
localized communication between mobile nodes and is expected to propel a
plethora of novel multimedia services. However, even though it offers a wide
set of capabilities mainly due to the proximity and resource reuse gains,
interference must be carefully controlled to maximize the achievable rate for
coexisting cellular and D2D users. The scope of this work is to provide an
interference-aware real-time resource allocation (RA) framework for relay-aided
D2D communications that underlay cellular networks. The main objective is to
maximize the overall network throughput by guaranteeing a minimum rate
threshold for cellular and D2D links. To this direction, genetic algorithms
(GAs) are proven to be powerful and versatile methodologies that account for
not only enhanced performance but also reduced computational complexity in
emerging wireless networks. Numerical investigations highlight the performance
gains compared to baseline RA methods and especially in highly dense scenarios
which will be the case in future 5G networks.Comment: 6 pages, 6 figure
Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods
Copyright @ 2010 Elsevier B.V. All rights reserved.In this paper, the quality of service multicast routing and channel assignment (QoS-MRCA) problem is investigated. It is proved to be a NP-hard problem. Previous work separates the multicast tree construction from the channel assignment. Therefore they bear severe drawback, that is, channel assignment cannot work well with the determined multicast tree. In this paper, we integrate them together and solve it by intelligent computational methods. First, we develop a unified framework which consists of the problem formulation, the solution representation, the fitness function, and the channel assignment algorithm. Then, we propose three separate algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and tabu search). These three algorithms aim to search minimum-interference multicast trees which also satisfy the end-to-end delay constraint and optimize the usage of the scarce radio network resource in wireless mesh networks. To achieve this goal, the optimization techniques based on state of the art genetic algorithm and the techniques to control the annealing process and the tabu search procedure are well developed separately. Simulation results show that the proposed three intelligent computational methods based multicast algorithms all achieve better performance in terms of both the total channel conflict and the tree cost than those comparative references.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
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