Hassan R. Barada

Resource allocation in macrocell-femtocell network using genetic algorithm

In this paper, we consider the problem of resource allocation in two-tier networks taking into consideration nondense femtocell deployments. The following limitations can be remarked from the prior work in the field of resource allocation: (1) resources are underutilized due to the equal power distribution in macrocell, (2) access to public users in femtocells is restricted to avoid depriving own subscribers transmissions, and (3) degradation of signal-to-noise ratio due to noise effects has not been evaluated. To overcome these limitations, we propose a joint power and bandwidth allocation among two tiers together with base station selection using genetic algorithm. Our solution is able to: (1) maximize the overall system throughput, (2) find an appropriate serving base station for each user, and (3) bandwidth and power assigned to each user. Simulations were conducted and a comparison with a Weighted Water Filling algorithm is carried out.

A Framework for an End-to-End Secure Wireless Smart Home System

This paper describes a complete framework for an end- to-end smart home monitoring and control system. The communication with all components of the system is done using a variety of wireless technologies. The system incorporates security features at both ends that prevent unauthorized access as well as assign privileges to the users. The complete prototype smart home system was implemented and it supports three main services: monitoring the status of devices, controlling their settings through configurations that are device dependent, and periodic notification of the status of devices. The prototype system has a modular structure that enables the incorporation of additional components and services.

Energy-Efficient Resource-Allocation Model for OFDMA Macrocell/Femtocell Networks

Femtocells (FCs) are introduced to enhance the indoor coverage and system capacity of traditional cellular networks. However, the network performance could be significantly deteriorated due to the increase in cochannel interference in dense deployment. In the literature, three spectrum usage schemes that deal with the cross-tier interference have been proposed, i.e., orthogonal assignment, underlay, and controlled underlay using mainly closed-access FCs. This paper targets the optimization of resource allocation, together with the selection of base stations (BSs) in two-tier networks assuming hybrid access FCs, to reduce the cross-tier interference. Moreover, this model aims to achieve effective spatial reuse between the macrocell (MC) and FCs while guaranteeing quality-of-service (QoS) transmissions by means of joint power control in both tiers. Simulations are conducted to show a comparison with the other three approaches.

Self-Optimization of Pilot Power in Enterprise Femtocells Using Multi objective Heuristic

Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration.

Deployment challenges of femtocells in future indoor wireless networks

Femtocell technology is expected to improve both coverage and capacity, especially indoors. Providers of cellular services are increasingly implementing this technology in their networks. However, the decentralized manner of femtocells operation introduces several challenges. This paper presents an overview of the femtocells. It highlights the network requirements for deploying femtocells and discusses the major issues that affect their operation and performance. The paper also describes the key challenges of deploying femtocells and some of the proposed solutions. It then presents the future direction on applying optimization to enhance femtocell performance.

A methodology for algorithm regularization and mapping into time-optimal VLSI arrays

Abstract This paper provides a fairly comprehensive treatment of a broad class of algorithms as it pertains to systolic implementation. We describe some formal algorithmic transformations that can be utilized to map regular and some irregular compute-bound algorithms into the best-fit time-optimal systolic architectures. This methodology uses the concept of dependence vectors to order in time and space the index points representing the algorithm. However, by differentiating between two types of dependence vectors, the ordering procedure is allowed to be flexible and time optimal. Furthermore, the approach reported here deals with variable as well as fixed dependence vectors and does not put constraints on the topology or dimensionality of the target architecture. The ordered index points are represented by nodes in a diagram called Systolic Precedence Diagram (SPD). The SPD is transformed into a directed graph called the Systolic Directed Graph (SDG) which can be projected along defined directions to obtain the target architectures. If more than one valid projection direction exist, different designs are obtained. The resulting architectures. If more than one valid projection an improvement in the performance can be achieved by increasing PE fan-out. If so, the method provides the corresponding systolic implementation. The methodology has been tried on many signal processing, image processing, and graph theory algorithms and new arrays were designed as a result. In this paper, the methodology is illustrated by mapping three problems, namely, vector-matrix multiplication, matrix-matrix multiplication, and transitive closure problems into many planar and nonplanar time-optimal VLSI arrays.

Macrocell–femtocells resource allocation with hybrid access motivational model

Abstract Femtocell technology has emerged as an efficient cost-effective solution not only to solve the indoor coverage problem but also to cope with the growing demand requirements. This paper investigates two major design concerns in two tier networks: resource allocation and femtocell access. Base station selection together with dual bandwidth and power allocation among the two tiers is investigated under shared spectrum usage. To achieve fair and efficient resource optimization, our model assumes that the hybrid access mode is applied in the femtocells. The hybrid access mode is beneficial for system performance as (1) it lessens interference caused by nearby public users, (2) it allows public users to connect to near femtocells and get better Quality of Service (QoS) and (3) it increases system capacity as it allows the macrocell to serve more users. However, femtocells’ owners can behave selfishly by denying public access to avoid any performance reduction in subscribers’ transmissions. Such a problem needs a motivation scheme to assure the cooperation of femtocells’ owners. In this paper, we propose a game-theoretical hybrid access motivational model. The proposed model encourages femtocells’ owners to share resources with public users, thus, more efficient resource allocation can be obtained. We optimize the resource allocation by means of the Genetic Algorithm (GA). The objective of the formulated optimization problem is the maximization of network throughput that is calculated by means of Shannon’s Capacity Law. Simulations are conducted where a modified version of the Weighted Water Filling (WWF) algorithm is used as a benchmark. Our proposed model, compared to WWF, achieves more efficient resource allocation in terms of system throughput and resources utilization.

Genetic algorithm based resource allocation and interference mitigation for OFDMA macrocell-femtocells networks

In this paper, we propose a base station selection and resource allocation model for OFDMA macro-femtocell networks. Dense deployment of femtocells can cause severe interference for femto and macro users alike. Our framework assumes hybrid-access femtocells to grant access to public user in their vicinity and to reduce interference perceived by femto users. Full spectrum sharing is investigated for the purpose of increasing system capacity. The proposed model aims to maximize the network throughput for a given interference threshold. To do so, the model determines the best serving base station based on link conditions. Genetic Algorithm is used to solve the resource optimization problem by finding the appropriate bandwidth and power assignments for each user. Simulations were conducted and a comparison with a modified version of the Weighted Water Filling algorithm is presented.

Optimum embeddings of end-around meshes into pyramid networks

To increase the utility of task-oriented or specialized parallel networks, especially those already constructed, one can simulate one parallel network on another. Of primary interest then is the lower bound on the cost of such a simulation. The authors show that end-around meshes can be mapped to pyramids with dilation two, and minimal expansion. Furthermore, they show that if all computations are synchronized using a global clock, one can achieve an effective edge congestion of one. In this manner, they remove the need for scheduling or queueing of messages since each communication link hosts at most one message path per cycle. Hence, they obtain essentially a contention-free simulation of one parallel interconnection network by another. They also prove that this embedding is optimum, i.e. end-around meshes are not subgraphs of pyramids.<<ETX>>

QoS-OLSR protocol based on intelligent water drop for Vehicular ad-hoc networks

In this paper, we address the problem of MultiPoint Relay (MPR) node disconnection due to mobility in Vehicular ad-hoc networks (VANETs) using the cluster-based Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. The protocol uses MPR nodes to establish communication among the clusters. MPR disconnection represents a major challenge in VANETs, due to the frequent change in the network topology. Consequently, the performance of the routing protocol will be weakened as it adversely affects the connectivity level of the network. Thus, our solution is a new cluster-based QoS-OLSR protocol based on intelligent water drop algorithm that is capable of (1) selecting the best set of MPR in terms of QoS (2) dealing with the MPR disconnection as it selects alternatives to assure a connected network (3) maintaining a reliable MPR failure management process. Simulation results demonstrate that the proposed model succeeds in improving the network connectivity and stability, reducing both the overhead and the path length, and increasing the packet delivery ratio compared to the original QoS-OLSR.