Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication

Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput and extending battery life of user equipments. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered as bidders that compete for channel resources, and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication, and present numerical simulations to verify the proposed algorithm.Comment: IEEE Transactions on Wireless Communication

Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity

Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's

Frequency Plan Optimization Based on Genetic Algorithms for Cellular Networks

Cellular networks are constantly evolving to ensure a better Quality of Service (QoS) and quality of coverage ever more important. The radio cellular systems are based on frequency allocation. In this context, frequency allocation principle consists in choosing an optimal frequency plan to meet traffic demand constraints and communication quality while minimizing the radio interferences. This paper proposes an optimal frequency allocation approach based on genetic algorithms to minimize co-channel and adjacent channel interference. The validation of this new approach is confirmed by the results of the work we have done in the GSM network. In fact, we used the file obtained by the OMC-R, which defines the adjacent cells of each cell and the frequencies allocated to the considered area. The results obtained clearly show the effectiveness and robustness of the approach used

Multi-radio Channel Allocation in Competitive Wireless Networks

Channel allocation has been extensively studied in the framework of cellular networks, but the emergence of new system concepts, such as cognitive radio systems, bring this topic into the focus of research again. In this paper, we provide a formal analysis of the selfish multi-radio channel allocation problem using game theory. We conclude that in spite of the non-cooperative behavior of such devices, their channel allocation results in a Pareto- and system-optimal solution. Furthermore, we present a simple algorithm to achieve this efficient channel allocation. To the best of our knowledge, our paper is the first contribution to this important topic

Channel assignments using constrained greedy algorithm, T-coloring and simulated annealing in mesh and cellular networks

Channel assignment is an important step in communication networks. The objectives of minimizing networks interference and the channels used are the problems in the channel assignments of the networks. In real environments, some difference will be expected in the performance of the networks when the channel allocation algorithms under more accurate interference models are deployed. In this research, the wireless mesh networks represent dynamic networks while static networks are represented by the cellular networks. In the wireless mesh networks, communication between a pair of nodes happens when both nodes are assigned with channels. The cellular networks are the radio network distributed over land areas called cells, each served by at least one fixed-location transceiver. Channel assignments in the networks is an application of the vertex coloring in graph theory. Previously, the Greedy Algorithm was used for link scheduling but only the adjacent channel constraint was considered. Here, an algorithm called Improved Greedy Algorithm was proposed to solve the channel assignments by considering the adjacent channel and co-channel constraints which is an improvement to the algorithm. Besides, Simulated Annealing and T-coloring problem are combined to minimize the channels used. The algorithms are applied for single and multiple channels communications in the wireless mesh networks and cellular networks to show the different results of the channel assignments. Further improvement is made on the multiple channels case where the Improved Greedy Algorithm is applied by considering the cosite constraint in addition to the co-channel and adjacent channel constraints. The Improved Greedy Algorithm has been tested in a series of simulations. Results for the simulations prove that the Improved Greedy Algorithm perform significantly well for the channel assignment problem

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

Data Aggregation in Capillary Networks for Machine-to-Machine Communications

As machine-to-machine applications using cellular systems become pervasive, it is an important concern that their deployment does not jeopardize the performance of the cellular systems. Support for a massive number of machines brings technical challenges affecting the performance of the random access channel and efficiency of radio resource allocation. Capillary networks are considered as an extensions to the cellular systems for providing large-scale connectivity. This paper proposes an aggregation scheme for capillary networks connected to the LTE network to improve their communication efficiency. A gateway, an intermediate unit between machines and the base station, aggregates packets from the machines during a predefined time, and then delivers them to the LTE network. In addition, this paper analyzes the trade-offs between random access interaction, resource allocation, and communication latency. Results reveals that accepting the extra latency for accumulating packets can significantly reduce the random access requests and the required resources for the data transmissions.Peer reviewe

Predicting a User's Next Cell With Supervised Learning Based on Channel States

Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.Comment: The 14th IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC), Darmstadt : Germany (2013