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

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201

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

QoS constrained cellular ad hoc augmented networks

In this dissertation, based on different design criteria, three novel quality of service (QoS) constrained cellular ad hoc augmented network (CAHAN) architectures are proposed for next generation wireless networks. The CAHAN architectures have a hybrid architecture, in which each MT of CDMA cellular networks has ad hoc communication capability. The CAHAN architectures are an evolutionary approach to conventional cellular networks. The proposed architectures have good system scalability and high system reliability. The first proposed architecture is the QoS constrained minimum-power cellular ad hoc augmented network architecture (QCMP CAHAN). The QCMP CAHAN can find the optimal minimum-power routes under the QoS constraints (bandwidth, packet-delay, or packet-error-rate constraint). The total energy consumed by the MTs is lower in the case of QCMP CAHAN than in the case of pure cellular networks. As the ad hoc communication range of each MT increases, the total transmitted power in QCMP CAHAN decreases. However, due to the increased number of hops involved in information delivery between the source and the destination, the end-to-end delay increases. The maximum end-to-end delay will be limited to a specified tolerable value for different services. An MT in QCMP CAHAN will not relay any messages when its ad hoc communication range is zero, and if this is the case for all MTs, then QCMP CAHAN reduces to the traditional cellular network. A QoS constrained network lifetime extension cellular ad hoc augmented network architecture (QCLE CAHAN) is proposed to achieve the maximum network lifetime under the QoS constraints. The network lifetime is higher in the case of QCLE CAHAN than in the case of pure cellular networks or QCMP CAHAN. In QCLE CAHAN, a novel QoS-constrained network lifetime extension routing algorithm will dynamically select suitable ad-hoc-switch-to-cellular points (ASCPs) according to the MT remaining battery energy such that the selection will balance all the MT battery energy and maximizes the network lifetime. As the number of ASCPs in an ad hoc subnet decreases, the network lifetime will be extended. Maximum network lifetime can be increased until the end-to-end QoS in QCLE CAHAN reaches its maximum tolerable value. Geocasting is the mechanism to multicast messages to the MTs whose locations lie within a given geographic area (target area). Geolocation-aware CAHAN (GA CAHAN) architecture is proposed to improve total transmitted power expended for geocast services in cellular networks. By using GA CAHAN for geocasting, saving in total transmitted energy can be achieved as compared to the case of pure cellular networks. When the size of geocast target area is large, GA CAHAN can save larger transmitted energy

Integrated channel assignment and power control in cellular networks using hill-climbing approach.

Recent year\u27s incredible success and exponential growth of wireless cellular network services have necessitated careful management of radio resources to improve system capacity. Mainly due to the insufficiency of radio spectrum, reuse or sharing of radio frequency must be considered. In practical, the sharing of radio frequency introduces interferences among users, which in turn limit the system capacity. On the other hand, control of transmitter power can suppress co-channel interference, adjacent channel interference and limits the consumption of power. Thus channel assignment and power control are two effective means in wireless cellular networks and they are highly correlated to each other. Most of the existing papers have focused on optimizing the assignment of channels assuming that the allocation of transmitter power is known and fixed or vice-versa. In this thesis, we study the integration of channel assignment and power control simultaneously to increase the network capacity and throughput. We have proposed a new channel assignment approach, called HCA-PC (Hybrid Channel Assignment + Power Control) using dynamic reuse distance concept to optimize the channel assignment. We develop a Hill-climbing approach with random restart strategy, using an efficient problem representation and a fitness function that optimizes channel assignment and power control in the cellular network. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .V52. Source: Masters Abstracts International, Volume: 44-03, page: 1392. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Optimal channel assignment and power control in wireless cellular networks

Wireless mobile communication is a fast growing field in current telecommunication industry. In a wireless cellular network, channel assignment is a mechanism that assigns channels to mobile users in order to establish a communication between a mobile terminal and a base station. It is important to determine an optimal allocation of channels that makes effective use of channels and minimizes call-blocking and call-dropping probabilities. Another important issue, the power control, is a problem of determining an optimal allocation of power levels to transmitters such that the power consumption is minimized while signal quality is maintained. In wireless mobile networks, channels and transmitter powers are limited resources. Therefore, efficient utilization of both those resources can significantly increase the capacity of network. In this thesis, we solve such optimizations by the hybrid channel assignment (HCA) method using integer linear programming (ILP). Two novel sets of ILP formulation are proposed for two different cases: Reuse Distance based HCA without power control, and Carrier-to-Interference Ratio based HCA combined with power control. For each of them, our experimental results show an improvement over other several approaches

The Game Theory: Applications in the Wireless Networks

Recent years have witnessed a lot of applications in the computer science, especially in the area of the wireless networks. The applications can be divided into the following two main categories: applications in the network performance and those in the energy efficiency. The game theory is widely used to regulate the behavior of the users; therefore, the cooperation among the nodes can be achieved and the network performance can be improved when the game theory is utilized. On the other hand, the game theory is also adopted to control the media access control protocol or routing protocol; therefore, the energy exhaust owing to the data collision and long route can be reduced and the energy efficiency can be improved greatly. In this chapter, the applications in the network performance and the energy efficiency are reviewed. The state of the art in the applications of the game theory in wireless networks is pointed out. Finally, the future research direction of the game theory in the energy harvesting wireless sensor network is presented

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique