Distributed and Adaptive Channel Allocation Algorithm for a Wireless Mesh Network with Directional Antennas

Adaptive channel and power allocation of downlink multi-user MC-CDMA systems

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the performance of the corresponding channel allocation algorithm is important. Such a study enables designers to arrive at the optimal utility function to be used in game-theory based channel allocation algorithms, and the same is explored to the best extent, in this paper. The detailed procedure of allocating channels to all the contending nodes through game-based channel allocation has been discussed in this paper. Moreover, the performance of six different utility functions proposed which can be used for channel allocation using game theory has been evaluated for respective performances through MATLAB-based simulations.

Channel allocation plays an important role in increasing the throughput and assuring fairness to users not only in traditional cellular networks but also in cognitive radio networks and mesh networks. In this paper, we study the effect of pre-allocating channels to cells with respect to a distributed and fault-tolerant channel allocation algorithm. Results from our extensive performance evaluation indicate that distributed channel allocation algorithms that pre-allocate all channels to cells can have lower call failure rate, call blocking rate, and handoff drop rate compared to algorithms that partially pre-allocate channels or does not pre-allocate channels.

One of the fundamental problems in a cellular phone network is to assign frequencies to base stations in a such way that interference in the system is minimized. A decentralized adaptive channel allocation algorithm for D-AMPS is analyzed. Base stations select frequencies based on repeated interference and bit error rate measurements on up- and downlink. Extensive simulations demonstrate good performance in terms of C/I-levels, convergence rate and stability. Simulation results also point to the need for downlink measurements when certain base stations have a fixed channel allocation.

For reducing the channel switching frequency due to uncertain and frequent primary users' interruptions, we design a nonparametric channel allocation algorithm which can effectively manage the spectrum resources in distributed cognitive radio networks and serve for delay-sensitive data traffics. In the proposed algorithm, an infinite Gaussian mixture model is used to get the posterior decision from the feature spaces of the channel idle duration and the primary user's frequency channel, while the collapsed Gibbs sampling is applied for fast convergence. Experimental and simulation results demonstrate that the proposed channel allocation algorithm outperforms the random channel allocation method and the idle probability based channel allocation algorithm in terms of throughput and packet drops.

The authors consider the problem of dynamic channel allocation in cellular networks. Each cell can use any channel, subject to the interference constraints. Channel allocation algorithms are executed by the network switch in a centralized way. The authors show how to design and use objective functions aimed at proper channel allocation and improvement of network performance. As a figure of merit of network performance, they consider the blocking probability in the network as a whole, and the maximum blocking probability in any particular cell of the network ("hot-spot" in the network). They designed three specific channel allocation policies, based on three different objective functions. Compared with two other benchmark policies, the approach shows significant improvement. Performance analysis of various channel allocation policies is virtually impossible without simulations, which are prohibitively time-consuming in the case of small blocking probabilities. The authors propose an original approximate method based on a short simulation and an analytic approximation. The method exhibits good accuracy and significant improvement in efficiency.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we deal with competitive local on-line algorithms for non-preemptive channel allocation in mobile networks. The signal interferences in a network are modeled using an interference graph G. We prove that the greedy on-line algorithm is Δ-competitive, where Δ is the maximum degree of G. We employ the “classify and randomly select” paradigm ([5, 12]), and give a 5-competitive randomized algorithm for the case of planar interference graphs. We show that the problem of call control in mobile networks with multiple available frequencies reduces to the problem of call control in networks with a single frequency. Using this reduction, we present on-line algorithms for general networks with a single frequency. We give a local on-line algorithm which is \(\frac{{\alpha (\delta + 1 + \alpha )}}{{(1/2 + \alpha )^2 }}\)-competitive, where a is the independence number of G and δ is the average degree of G. The above results hold in the case that the duration of each request is infinite, and the benefit the algorithm gains by accepting each request is equal to 1. They are extended to handle requests of arbitrary durations, and arbitrary benefits. To our knowledge, this is the first time that competitive analysis is employed to study on-line algorithms for the channel allocation problem in mobile networks.

Delay issue is a challenge in multi-hop cognitive radio networking because of the dynamic change of channels available to the cognitive radio nodes. To address this challenge, we propose a channel allocation and rate allocation scheme with low complexity in this paper. Our scheme is flexible so that it can react to the dynamic change of channel availability, and it can minimize the delay by considering the primary user activities. First, we model the primary users' activity, channel availability and the interference among the cognitive radio nodes in a cognitive radio network environment, and show how to implement a channel allocation algorithm and a rate allocation scheme in multi-hop cognitive radio networks. Second, we formulate an optimization problem to minimize the end-to-end delay of the network. We consider the channel availability constraint and the primary user activities jointly, and analyze the end-to-end delay in multi-hop cognitive radio networks. In order to reduce the computing complexity, we propose a graph coloring based channel allocation and rate selection algorithm using gradient descent method. Furthermore, we show the performance of our schemes and compare them with existing schemes for different scenarios of channel availability and number of channels through simulations. Our work brings insights on how to make rate selection and channel allocation in multi-hop cognitive radio networks.

We discuss the wireless channel allocation problem for data dissemination in mobile computing systems. Methods for accessing data through broadcast and on-demand channels are described. We provide analytical models and cost formulae for the exclusive broadcast channels and the exclusive on-demand channels and propose a dynamic channel allocation algorithm for optimizing system performance. Our performance evaluation shows that dynamic channel allocation significantly improves system performance and the channel allocation algorithm gives us the optimal solution for various system parameter settings.

Previous studies of Dynamic Synchronous Transfer Mode (DTM), which is based on multi-access dual fiber bus, showed that it could be a viable technology in the future. This paper points out that the characteristics of active and passive nodes are not balanced and the performance of the network can be increased by decreasing the need for slot allocation during call set-up. We propose a new type of smoothing algorithms, which is a new channel allocation algorithm that improves the fairness of DTM network with proper priority settings and the performance of channel allocation in ordinary operating conditions. Results are based on simulation, which was carried out by a self-developed simulator.

We propose a distributed channel allocation algorithm based on a threshold scheme, called D-CAT, for cellular mobile networks. The algorithm employs two thresholds: (i) a heavy threshold for determining whether a cell is heavy, or overloaded, and for triggering the channel allocation algorithm; and (ii) a target threshold for indicating the target number of free channels that a heavy cell intends to acquire. Based on the two-threshold scheme, the D-CAT algorithm can determine the optimal number of free channels as well as the cell(s) from where a heavy cell should import channels in order to satisfy the required channel demand. Simulation experiments and analyses show that the proposed algorithm incurs lower overhead for channel allocation and is more efficient in terms of channel utilization than other distributed channel allocation algorithms. It also outperforms other centralized and distributed algorithms in terms of call blocking probability.

Rapid advances of the handheld devices and the emergence of the demanding wireless applications require the cellular networks to support the demanding user needs more effectively. The cellular networks are expected to provide these services under a limited bandwidth. Efficient management of the wireless channels by effective channel allocation algorithms is crucial for the performance of any cellular system. To provide a better channel usage performance, dynamic channel allocation schemes have been proposed. Among these schemes, distributed dynamic channel allocation approaches usually have better performance results. The two important issues that must be carefully addressed in such algorithms are the efficient co-channel interference avoidance and messaging overhead reduction. One of these distributed channel allocation algorithms is the DonorList algorithm, which can effectively manage these two issues. In this paper the channel import efficiency of the DonorList algorithm is evaluated. The performance evaluation results obtained after extensive simulation studies, which were conducted under different traffic load conditions, show that the proposed algorithm provides improvement over the performance of other algorithms recently proposed in the literature.

Cognitive radio is an optimistic technology to implement the concept of dynamic spectrum access and provide a flexible way to share the spectrum among the primary and secondary users. In this paper, a game theoretic-based model is presented using the concept of Nash Equilibrium for spectrum sharing. In this model, interference and number of radios on each link are considered as parameters for designing the game. An algorithm for channel allocation among the users is also presented. From the simulation analysis, it is observed that the system performs satisfactorily in terms of network utilization. Also, the Taguichi method is applied and an analysis of variance (ANOVA) is performed, proving that the design parameters taken into consideration in our proposed method are impactful.

Proposing a centralized algorithm to minimize message broadcasting energy in wireless sensor networks using directional antennas

We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an E O d / ε time 1 − ϵ -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a 1 − ϵ , O 1 -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy O 1 -approximation algorithm for the multichannel scheduling problem and an O 1 , O 1 -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods.