Spectrum management in wireless networks

The limited spectrum provided by the IEEE 802.11 standard is not efficiently utilized in the existing wireless networks. The inefficiency comes from three issues in spectrum management. First, the utilization of the available non-overlapping channels is not evenly distributed, that is, closely deployed users tend to congregate in the same or interfering channels. This issue incurs an excessive amount of co-channel interference (CCI), causing collisions, and thus decreases network throughput. Second, the dynamic radio channel allocation (RCA) problem is non-deterministic polynomial-time hard (NP-hard). The employed heuristic optimization methods can not efficiently find a global optimum, including simple minimization or maximization processes, or certain slow learning processes. Third, the default transmission power of a user reserves unnecessarily large deference areas, in which the collision avoidance (CA) mechanisms prohibit simultaneous transmissions in a given channel. Consequently, the spatial channel reuse is significantly reduced.;To solve the first issue, we propose an annealing Gibbs sampling (AGS) based distributive RCA (ADRCA) algorithm. The ADRCA algorithm has the following advantages: (1) It uses average effective channel utilization (AECU) to evaluate the channel condition. AECU has a simple relationship with CCI and can accurately reflect the channel congestion conditions. (2) It employs the AGS optimization method, which divides a global optimization problem into a set of distributed local optimization problems. Each of those problems can be solved by simulating a Markov chain. The stationary distribution of the Markov chains is a globally optimized solution. (3) It includes three different cases, namely AGS1, AGS2 and AGS3, which adapt to various types of wireless networks with different optimization objectives. AGS1 is designed to search for a global optimal channel assignment in OIS networks; AGS2 is proposed to work in NOIS networks and pursue maximum individual performance. Added with a prerequisite for RCA procedures, AGS3 focuses on cost-effectiveness, reduces channel reallocation attempts, and enhances system stability without significantly downgrading its optimization performance. To further study the cost-effectiveness of ADRCA, an upper limit of the computational scale (CS) is found for AGS3 based on an innovative neighboring relationship model in a practical network scenario.;To solve the second issue, we propose a hybrid approach to study the computational scale (CS), which is defined as the number of channel reallocations until a network reaches a convergent state. First, we propose a simple relationship model to describe the interference relation between an AP and its neighboring APs. Second, for one of the simplest cases in the relationship model, we find an analytical solution for the CS and validate it by simulations. Third, for more general cases, we combine the cases with a similar CS means by using one-way analysis of variance (ANOVA) and find the upper bound of the CS with extensive simulations. The simulation results demonstrate that the hybrid approach is simple and accurate as compared to traditional intuitive comparison methods. Based on the aforementioned hybrid approach, an upper limit of the CS is found for AGS3 in a practical network scenario.;To solve the third issue and also raise the limit of throughput density, we propose the channel allocation with power-control (CAP) strategy which integrates the ADRCA algorithm and the digitized adaptive power control (DAPC) algorithm, to achieve a synergetic benefit between power control and RCA, which is not considered by the existing RCA algorithms.;The synergy comes from the following two aspects: (1) By reducing the transmission power of each node, DAPC can lower CCI levels, allow more simultaneous transmissions within a certain area, increase spatial reuse, and raise the limit of the throughput density. It also reduces the number of nodes competing for a given channel, and thus significantly decreases the CS of ADRCA. (2) By striving to assign interfering neighbors to non-overlapping channels, ADRCA minimizes the number of hidden terminals introduced by the power control processes.;In this work, we have conducted extensive simulations to demonstrate the effectiveness of the proposed methods. Our simulation results show that AGS1 can achieve a global optimum in most OIS network scenarios. With a 95% confidence level, it achieves 99.75% of the global maximum throughput. AGS2 performs on par with AGS1 in NOIS networks. AGS3 reduces the CS by as much as 98% compared to AGS1 and AGS2. The simulation results also demonstrate that compared with the standard MAC protocol, CAP increases the overall throughput by up to 9.5 times and shortens the end-to-end delay by up to 80% for UDP traffic. (Abstract shortened by UMI.)...