Volume 21, Issue 2, August 2013

An Upper Bound for the Link Efficiency of Selective-Repeat ARQ Considering Unreliable Acknowledgements [Full Text]

K D R Jagath-Kumara

Recent research on high efficiency data links includes a variety of hybrid-ARQ schemes, which are almost always based on the basic selective-repeat ARQ (SR-ARQ) scheme. Therefore, it is worthwhile to establish performance bounds of SR- ARQ clearly. If the acknowledgements are reliable, as it is usually assumed, the efficiency of such a scheme is Pc where Pc is the probability that a received frame is error-free. In refining SR-ARQ, this paper shows that the said upper bound of the link efficiency reduces to  Pc2   if the acknowledgements are unreliable as in reality.

Design and Analysis of a 2.4 GHz, Fifth- Order Chebyshev Microstrip LPF [Full Text]
Talib Mahmood Ali

Abstract- In this paper, a design of miniature fifth order Chebyshev lowpass filter is presented. The filter consists of Flame Retardant 4 (FR-4) as a substrate having a thickness of 1.6 mm, loss tangent of 0.027 and relative permittivity of 4.5. The proposed filter with basic stepped impedance structure, the width and length of low and high characteristic impedance was calculated based on microstrip analysis. The design was simulated and optimized using CST Microwave Studio. The best simulated attenuation S11 response was observed at 2.45 GHz with a value of -26.4 dB for 0.01 ripple while the corresponding Insertion Loss S21 is -0.01dB. The proposed fifth-order filter also has the merits of small circuit area. Validation of the LPF design was obtained via good agreement between the theoretical analysis and simulation results. 


Design a Hybrid System Geno-Neuro-Fuzzy Controller for Dynamic Load Balancing in Wireless Ad- hoc Networks [Full Text]

Hussein A.Lafta

Abstract - Congestion at the link and in the nodes is the main cause of a large delay in the ad hoc networks, where band width is limited. Balancing the work among the network nodes will be one of the best solutions. Once, the source node may be has selected a set of paths to destination, it can send data to a destination along unloaded path nodes. Load balanced routing aims to move traffic from the areas that are above the optimal load to less loaded areas, so that the entire network achieves better performance. If the traffic is not distributed evenly, then some areas in a network are under heavy load while some are lightly loaded or idle. Therefore good load balancing algorithms must be fast and should not add heavy cost, because complexity of these algorithms in communication channels incurs ambiguity, causes uncertainty in decision making. A novel approach based on artificial intelligence field such as neural networks, fuzzy logic, and genetic algorithm is suggested in this work. The integration of these subsystems gives a system benefits from the advantages of each subsystem and encroaches on the disadvantages. All the parameters of the controller is tuned and learned by genetic algorithm. This controller is based on back propagation neural network. This can eliminate laborious design steps such as manual tuning, of the membership functions and selection of the fuzzy rules and weights of neural network, which give the neural network a greater ability to generalize and accelerate the convergence process and prevent the network to get stuck in a local minimum.