Mohammed El-Absi

ABSTRACT In this paper, we present a radio resource allocation algorithm based on interference alignment (IA) for orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) based MIMO cognitive radio (CR) systems. The algorithm provides the opportunity for all secondary users to share the available subcarriers simultaneously using IA technique. Besides, it allocates the power budget of each secondary user over the subcarriers in order to maximize the sum-rate of the system without inducing excessive interference to primary users. Simulations show that CR systems based on IA achieves a significant sum-rate increase compared to CR systems based on frequency division multiple access (FDMA). Moreover, IA based CR using FBMC physical layer achieves considerable throughput improvement compared to OFDM physical layer.

ABSTRACT Main objective of this contribution is to apply Interference Alignment (IA) algorithms in real-world indoor environments for UWB MIMO MB-OFDM communication systems. In indoor environments, the required orthogonality between multi-users channels, which is necessary for proper IA, could be hardly reached. The spatial diversity among the users/nodes is mostly insufficient to obtain a robust performance for IA algorithms. In this work, a practical artificial channel diversity technique is applied through antenna selection to choose the best scenario providing the maximum orthogonality and consequentially the best overall system performance. Our analysis considers deterministic UWB MIMO channel model based on EM Ray-tracing in a real-world multi-user indoor environment. Simulation results present a significant enhancement in the overall system performance. Furthermore, the impact of the directional properties and the orientations of the antennas on the system are investigated.

ABSTRACT Abstract—In this paper, interference alignment (IA) is utilized to obtain an efficient and fair resource allocation algorithm in MIMO cognitive radio (CR) systems. In the proposed algorithm, IA enables all secondary users to share the available spectrum without affecting the quality-of-service of the primary system. The considered methodology increases the total degrees of freedom of the CR systems and achieves fairness among CR users. An optimal power allocation based on IA is formulated in order to maximize the total sum-rate while keeping the interference introduced to the primary system lower than the prescribed interference threshold. Furthermore, a sub-optimal power allocation scheme is proposed to overcome the high computational complexity of the optimal scheme. Simulations reveal that IA technique achieves significant sum-rate increase of CR systems compared to frequency division multiple access (FDMA) CR systems. Moreover, the sup-optimal algorithm approaches the optimal sum-rate performance.

ABSTRACT The main objective of this contribution is to develop a novel antenna selection algorithm for Interference Alignment (IA) in multi-user communication systems. Successive IA requires high degree of independency among the channels, which could hardly exist in real-world environments. Therefore, the Bit Error Rate (BER) performance of the IA system suffers from a dramatic degradation, especially in indoor environments. Applying the developed antenna selection algorithm can effectively increase the channels diversity and improve the BER performance. This selection algorithm based on selecting the maximum Canonical Correlation (CC) between the desired signal subspace and the interference-free subspace in order to maximize the average received Signal-to-Noise Ratio (SNR) of the system. The influence of the CC on sum-rate would be presented mathematically. Simulation results show a significant improvement of the BER system performance based on the CC antenna selection algorithm compared with the maximum sum-rate selection algorithm.

ABSTRACT The main objective of this contribution is to develop a new interference alignment (IA) algorithm, which improves the sum-rate performance of multiuser MIMO communication systems. The recent iterative IA approaches cannot guarantee robust sum-rate performance in different K-user MIMO interference channels, especially at high SNR regime. In our proposed distributed optimization algorithm, each receiver maximizes the desired signal power while preserving the minimum interference leakage as a constraint. A convex relaxation has been applied to this optimization problem after reformulating it into semidefinite programming form. This algorithm provides orthogonal precoders and decoders, which is fairly simple in practical implementation. Simulation results of the proposed algorithm proffer significant sum-rate improvement in various interference channels compared to existing algorithms.