Wireless personal area networks (WPANs) will play an important role in next-generation communication networks. Currently, two technologies are being considered for the physical layer of WPANs, based on the two ultra wideband (UWB) standards, namely, multiband orthogonal frequency division multiplexing (MB-OFDM) UWB and direct-sequence (DS) UWB. The coexistence issue of these two types of WPANs in the same coverage area, raises new issues and introduces new problems which should be dealt with to avoid performance degradation. In particular, efficient radio resource management (RRM) in such environments is challenging. Indeed, the coexistence of heterogenous UWB based WPANs (UPANs) has an ad hoc nature, which requires RRM approaches that are different from traditional infrastructure-based ones. In this paper, we propose new algorithms for two RRM modules in heterogeneous UPANs, namely, radio access technology (RAT) selection and vertical handoff (VHO). To improve the overall performance of the system, our design considers possible narrowband interference (NBI) in the environment as well as the link outage probability, in the decision process. We also provide an analytical model based on a 4D Markov process to study the system in equilibrium and derive the performance metrics, namely, the new-call and handoff-call blocking probabilities, throughput and average carried traffic. Numerical results and comparisons show that our design achieves enhanced performance in terms of throughput and grade of service (GoS).

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

We propose a new concept of network dimensioning, which is based not only on the grade of service but also on profit. In traditional network dimensioning methodology, the number of circuits on links is designed under a cost-minimization concept with grade of service constraints. Recently, telecommunication markets have become very large and competitive; therefore, we believe that a profit viewpoint is now essential. However, it is difficult to calculate profit in almost all the dimensioning methods currently used, because they mainly employ peak-hour traffic data, while profit depends on all the hourly traffic data which contain both peak and off-peak data. In this paper, we propose using all the hourly traffic data in network dimensioning. From these data and telephone charges for each hour, revenues will be estimated. On the other hand, facility costs will be estimated from the number of circuits. Finally, we can estimate profit from the difference between revenues and facility costs. Focusing on both quality and profits in network dimensioning leads to more advanced quality management and quality control in telecommunications networks than with traditional methodology. This paper outlines a dimensioning method based on profit, and describes its properties, some applications of it, and summarizes further studies.