Jamal S. Rahhal

A General Configuration Antenna Array for Multi-User Systems with Genetic and Ant Colony Optimization

Antenna arrays are used in the CDMA-based cellular systems in order to increase the systems capacity. Different approaches benefit from the spatial separation between users. Smart and adaptive beam antennas are the best proposed solution for these systems. Several methods are used to provide the system with a radiation pattern that increases the signal-to-interference ratio (C/I). Antenna configuration is the way the array elements are distributed in space. This distribution can influence the design of the beam formation or the adaptation method, such that, the calculation of the different parameters requires a known array configuration. Antenna configuration is discussed in several research papers including planar arrays and circular arrays. Here, we introduce a general antenna array structure and derived its optimal parameters, and then we optimize the solution under different quantization errors using a genetic algorithm (GA) and an ant colony optimization (ACO). Results showed that we can configure the antenna array to provide an acceptable C/I. Also, results showed that we can obtain a good solution that is less sensitive to quantization errors using the ACO.

Pre-Coding for MIMO Systems in Frequency-Selective Fading Channels

Multiple input multiple output (MIMO) systems showed good utilization of channel characteristics. In MIMO systems multiple signals are transmitted using multiple antenna system. This provides each receiver the combined signals and hence, array processing techniques helps in reducing the effects of interference among them. In this paper we devise the use of pre-coded MIMO system to reduce the effects of frequency selectivity and hence, enhance the systems capacity and/or reduce the bit error rate. In this technique we introduce a temporal pre-coder on each antenna signal; this creates a deterministic multi-path signals similar to signals received when the channel is multi-path fading channel. The same antenna signal will arrive at each receiver forming orthogonal sub-space and the receiver will be simple add and delay of the received signals. Ant colony optimization is used in this paper to select the best pre-code. Results showed that we can diagonalize the channel matrix and practically eliminate the interference for frequency selective fading channel. Simulation of two transmitting two receiving antennas pre-coded MIMO system showed that the capacity can be doubled.

LDPC coding for MIMO wireless sensor networks with clustering

Wireless Sensor Network (WSN) is used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. This leads to enhance the transmission and hence reduce energy consumed by the sensor. In MIMO systems multiple signals are combined at the transmitter and transmitted using multiple antennas. This provides each receiver the whole combined signal and hence, array processing techniques helps in getting better performance. To further enhance the transmission of data, a Low Density Parity Check (LDPC) Coded MIMO wireless sensor network is proposed. The system implements space diversity through Multiple Antennas and temporal diversity through LDPC code and uses a clustering procedure to optimize the forming of the MIMO system. Results showed that, if the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. And by controlling the encoder we can create a temporal and spatial code among the transmitted signals enhancing the BER results in longer battery life at sensor nodes.

Wireless MIMO Sensor Network with Power Constraint WLS/BLUE Estimators

Sensor networks are used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. In MIMO Sensor Network, multiple signals are transmitted from the sensors and multiple antennas are used at the control node. This provides each receiver the whole combined signal and hence, array processing techniques helps in reducing the effects of noise. In this paper we devise the use of MIMO sensor network and array decision techniques to reduce the noise effect. The proposed Constrained Best Linear Unbiased Estimator (CBLUE) and Constrained Weighted Least Square (CWLS) estimators showed good performance BER when used with MIMO Sensor Network. Most importantly these estimates showed good perturbation results when the estimated channel matrix is not accurate. The condition for good performance was to have the number of receiving antennas at the central node to be equal to the number of transmitting sensors and no significant improve was seen if the number of antennas is greater than the number of transmitting sensors. If the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. Enhancing the BER results in longer battery life at sensor nodes.

Two Slot MIMO Configuration for Cooperative Sensor Network

Sensor networks are used in various applications. Sensors acquire samples of physical data and send them to a destination node in different topologies. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. In MIMO Sensor Network, multiple signals are transmitted from the sensors and multiple sensors are used as receiving nodes. This provides each sensor multiple copies of the transmitted signal and hence, array processing techniques help in reducing the effects of noise. In this paper we devise the use of MIMO sensor network and array decision techniques to reduce the noise effect. The proposed system uses a transmission time diversity to form the MIMO system. If the number of sensors is large then groups of sensors will form the MIMO system and benefited from the diversity to reduce the required transmitted power from each sensor. Enhancing the BER reduces the required transmitted power which results in longer battery life for sensor nodes. Simulation results showed an overall gain in SNR that reaches 11 dB in some sensor network scenarios. This gain in SNR led to the opportunity of reducing the transmitted power by similar amount and hence, longer battery life is obtained.

Parallel Channels for High Rate UTRAN

The method proposed in this paper uses space–time filtering to support parallel channel transmission as an effort to solve two main limitations facing UMTS Terrestrial Radio Access Network (UTRAN): throughput limitation and capacity limitation at high bit rates. The overall system capacity in the case of a spreading factor of 4 was enhanced by adding four channels, leading to a total physical layer throughput of 6.5 Mbps with a slight reduction in the performance of less than 1 dB for some users, while other users performance remain without any degradation. In another case where a spreading factor 8 was used adding four parallel channels leads to a total throughput of 5.1 Mbps with only 1.5 dB reduction in the performance of some users. Finally in the case of a spreading factor of 16, with a reduction of less than 3 dB in performance, an addition of eight parallel channels leads to a throughput of about 5 Mbps.

A probabilistic model for sub-carrier allocation in OFDMA systems

Orthogonal frequency division multiple-access technique showed a successful utilization of channel features. It implements an orthogonal sub-carrier space to be shared among different users. The management of these sub-carriers in both power and frequency allocation is reflected on the systems performance as better utilization of bandwidth, and hence, better capacity is obtained. Sub-carrier allocation is used to avoid deep fading that might occur at some user’s locations but not at other user’s locations. In this paper, we devise an algorithm based on probabilistic model for sub-carrier allocation to avoid deep fading in some user’s signals. By controlling the sub-carrier allocation for each user, we can create a full rank channel for each user and hence, provide maximum capacity for the system. Simulation results showed that using the devised algorithm will avoid deep fading and utilize the bandwidth up to 40% more than localized allocation strategies.

Evolutionary Computation in Coded Communications: an Implementation of Viterbi Algorithm

Quantum Computing hopefully is the future of computing systems. It still on its first steps. The development of some quantum algorithms gives the quantum computing a boost on its importance. These algorithms (such as Shor’s and Grover’s algorithms) proved to have superior performance over classical algorithms [1-4]. The recent findings, that quantum error correction can be used, showed that the decoherence problem can be solved and hence the quantum computers can be realized [5-7]. The quantum algorithms are based on the use of special gates applied on one, two or more qubits (quantum bits). The classical computer uses different gates (NOT, AND, NAND, OR and XOR). Quantum gates are in many aspects different from classical gates where all gates must be reversible. This makes the quantum gates act as 2x2 transformation operators, where we have n input qubits and n output qubits. To understand the quantum bits and gates we describe the group of amplitudes that describes the state of a quantum register as a vector. A qubit with state 0 , which is

A Modified Genetic Algorithm for Training Adaptive Fuzzy Systems

Abstract Adaptive Fuzzy Logic Systems trained by genetic evolution of their pazameters are presented in this work. This technique is based on the aggregation of pazameter perturbations. Neither the evaluation function nor the membership functions, have to be differentiable as required in most optimization techniques. In the classical Genetic Algorithms, the solution space of each pazameter should be specified in the genetic search. The proposed technique does not specify the solution space of the pazameters of the fuzzy logic system. It specifies the ranges of the perturbations of the pazameters which will aggregate to fmd the optimum parameters for the Fuzzy Logic System. Computer simulation showed that the proposed technique reached an optimal solution for the Adaptive Fuzzy Logic parameters with a higher convergence rate than that of the classical GA.

Vieterbi decoder algorithm using quantum computing

Quantum computing promises a leap in performance over classical computing. Its computational power was revealed after the introduction of some quantum algorithms such as Grovers search algorithm. Many classical algorithms especially that deal with Error Correcting Codes are introduced to solve an exhaustive search problem. The quantum techniques promise an optimal search speed that is of special importance when the size of the searched domain is quite large. In this paper we devise the use of quantum search algorithm introduced by Grover to implement the viterbi algorithm (VA). Due to lack of resources and the nature of implementing quantum algorithms, only theoretical results are obtained. The use of quantum search algorithm showed that we can reduce the number of search computations exponentially. For example if the classical VA requires 262144 searching operations for a signalling length of 1024 intervals, the quantum algorithm will need only 512 searching operations when single search quantum viterbi algorithm (SSQVA) is used. And for the multi search quantum viterbi algorithm (MSQVA) it needs 16384 searching operations for the same signalling length.