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

Efficiency metrics computing in combined sensor networks

This paper discusses the computer-aided design of combined networks for offices and building automation systems based on diverse wired and wireless standards. The design requirements for these networks are often contradictive and have to consider performance, energy and cost efficiency together. For usual office communication, quality of service is more important. In the wireless sensor networks, the energy efficiency is a critical requirement to ensure their long life, to reduce maintenance costs and to increase reliability. The network optimization problem has been solved under considering of overall-costs as objective and quality of service including throughput, delay, packet losses etc. with energy efficiency as required constraints. This can be achieved by a combination of different planning methods like placement of wired and wireless nodes, tracing of cabling systems, energy-efficient sensor management and event-based sampling. A successful application of these methods requires a combined harmonized design at different levels of the networks. This paper aims to demonstrate how these methods are realized in the network planning. These tools provide optimized wired and wireless topologies under considering of costs, distances, transmitted power, frequencies, propagation environments and obstacles given in computer-aided design compatible formats

A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments

A Novel Airborne Self-organising Architecture for 5G+ Networks

Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned balloons or drones flying at low/medium/high altitude can be employed to enhance network coverage and capacity by deploying a swarm of flying platforms that implement novel radio resource management techniques. In this paper, we propose a novel layered architecture where NFPs, of various types and flying at low/medium/high layers in a swarm of flying platforms, are considered as an integrated part of the future cellular networks to inject additional capacity and expand the coverage for exceptional scenarios (sports events, concerts, etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful roll-out of the proposed architecture depends on several factors including, but are not limited to: network optimisation for NFP placement and association, safety operations of NFP for network/equipment security, and reliability for NFP transport and control/signaling mechanisms. In this work, we formulate the optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne Self-organising Network (SON) features. Our initial simulations show the NFP-LL can serve more User Equipment (UE)s using this placement technique.Comment: 5 pages, 2 figures, conference paper in IEEE VTC-Fall 2017, in Proceedings IEEE Vehicular Technology Conference (VTC-Fall 2017), Toronto, Canada, Sep. 201

Wireless coverage using unmanned aerial vehicles

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civilian application domains including real-time monitoring, search and rescue, and wireless coverage. UAVs can be used to provide wireless coverage during emergency cases where each UAV serves as an aerial wireless base station when the cellular network goes down. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. During such situations, the UAVs need to return periodically to a charging station for recharging, due to their limited battery capacity. Given the recharging requirements, the problem of minimizing the number of UAVs required for a continuous coverage of a given area is first studied in this dissertation. Due to the intractability of the problem, partitioning the coverage graph into cycles that start at the charging station is proposed and the minimum number of UAVs to cover such a cycle is characterized based on the charging time, the traveling time and the number of subareas to be covered by a cycle. Based on this analysis, an efficient algorithm is proposed to solve the problem. In the second part of this dissertation, the problem of optimal placement of a single UAV is studied, where the objective is to minimize the total transmit power required to provide wireless coverage for indoor users. Three cases of practical interest are considered and efficient solutions to the formulated problem under these cases are presented. Due to the limited transmit power of a UAV, the problem of minimizing the number of UAVs required to provide wireless coverage to indoor users is studied and an efficient algorithm is proposed to solve the problem. In the third part of this dissertation, the problem of maximizing the indoor wireless coverage using UAVs equipped with directional antennas is studied. The case that the UAVs are using one channel is considered, thus in order to maximize the total indoor wireless coverage, the overlapping in their coverage volumes is avoided. Two methods are presented to place the UAVs; providing wireless coverage from one building side and from two building sides. The results show that the upside-down arrangements of UAVs can improve the total coverage by 100% compared to providing wireless coverage from one building side. In the fourth part of this dissertation, the placement problem of UAVs is studied, where the objective is to determine the locations of a set of UAVs that maximize the lifetime of wireless devices. Due to the intractability of the problem, the number of UAVs is restricted to be one. Under this special case, the problem is formulated as a convex optimization problem under a restriction on the coverage angle of the ground users and a gradient projection based algorithm is proposed to find the optimal location of the UAV. Based on this, an efficient algorithm is proposed for the general case of multiple UAVs. The problem of minimizing the number of UAVs required to serve the ground users such that the time duration of uplink transmission of each wireless device is greater than or equal to a threshold value is also studied. Two efficient methods are proposed to determine the minimum number of UAVs required to serve the wireless devices