Journal-Communications and Networks

Communications & Networks

Load Balancing

Randomized Algorithms

Neural Networks

Various

Note: The papers on this website may differ from the published versions, both in format and in content.

1. R. Jordan, C.T. Abdallah and O. Dedeoglu, "Wireless Communications and Networking: An Overview", IEEE Antennas and Propagation Magazine, Vol. 44, No. 1, pp. 185-193, February 2002.   [pdf]  [ps]

   Abstract: This paper presents an overview of wireless local area networks (LANs) and wireless personal area networks (PANs), with emphasis on the two most popular standards: IEEE 802.11, and Bluetooth. While there are many such surveys in the current literature and online, we attempt here to present wireless LANs and PANS in a unified fashion as a viable alternative to wired LANs, while stressing the remaining challenges and limitations.




2. A. El-Osery, and C. T. Abdallah, "Distributed Power Control in CDMA Cellular System", Invited, IEEE Antennas and Propagation Magazine, Vol. 42, No. 4, pp. 152-169, August 2000.   [pdf]

   Abstract: In wireless cellular communication, it is essential to find effective means of power control of signals received from randomly dispersed users within one cell. Effective power control will heavily impact the system capacity. Distributed power control (DPC) is a natural choice for such purpose, because unlike centralized power control, DPC does not require extensive computational power. Distributed power control should be able to adjust the power levels of each transmitted signal using only local measurements, so that in reasonable time all users will maintain the desired signal to interference ratio. In this paper, we review different approaches for power control focusing on CDMA systems. We also introduce state-space methods and linear quadratic power control (LQPC) to solve the power control problem. A simulation environment was developed to compare LQPC with earlier approaches. The results show that LQPC is more effective and is capable of computing the desired transmission power of each mobile station in fewer iterations, as well as being able to accommodate more users in the system.




3. D. Docampo, D.R. Hush, and C. T. Abdallah, "Constructive Function Approximation: Theory and Practice", Intelligent Methods in Signal Processing and Communications, St. Louis, MO, pp.299-220, 1997.   [pdf]  [ps]

   Abstract: In this paper we study the theoretical limits of finite con­ structive convex approximations of a given function in a Hilbert space using elements taken from a reduced subset. We also investigate the trade­o# be­ tween the global error and the partial error during the iterations of the solution. These results are then specialized to constructive function ap­ proximation using sigmoidal neural networks. The emphasis then shifts to the implementation issues associated with the problem of achieving given approximation errors when using a finite number of nodes and a finite data set for training.