Mohammed El-Absi

Interference alignment with frequency-clustering for efficient resource allocation in cognitive radio networks

In this paper, we investigate the resource management problem in orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) systems. We propose performing resource allocation based on interference alignment (IA) in order to improve the spectral efficiency of CR systems without affecting the quality of service of the primary system. IA plays a role in the proposed algorithm to enable the secondary users (SUs) to cooperate and share the available spectrum, which leads to a considerable increase in the spectral efficiency of CR systems. However, IA based spectrum sharing is restricted to a certain number of SUs per subcarrier in order to satisfy the IA feasibility conditions. Accordingly, the resource allocation problem is formulated as a mixed-integer optimization problem, which is considered an

Antenna Selection for Reliable MIMO-OFDM Interference Alignment Systems: Measurement-Based Evaluation

\mathcal{NP}

Antenna selection for Interference Alignment based on subspace Canonical Correlation

-hard problem. To reduce the computational complexity of the problem, a two-phases efficient sub-optimal algorithm is proposed. In the first phase, frequency-clustering is performed in order to satisfy the IA feasibility conditions, where each subcarrier is assigned to a feasible number of SUs. Whenever possible, frequency-clustering stage considers the fairness among the SUs. In the second stage, the available power is allocated among the subcarriers and SUs without violating the constraints that limit the maximum interference induced to the primary system. Simulation results show that IA with frequency-clustering achieves a significant sum rate increase compared to CR systems with orthogonal multiple access transmission techniques.

Artificial diversity for UWB MB-OFDM Interference Alignment based on real-world channel models and antenna selection techniques

Interference alignment (IA) is a promising transmission technology enabling, essentially, the maximum achievable degrees of freedom (DOF) in K-user multiple-input-multiple-output (MIMO) interference channels. The ideal DOF of IA systems have been obtained using independent MIMO channels, which is, usually rarely observed in reality, particularly in indoor environments. Therefore, the data sum rate and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation between MIMO channels decreases the signal-to-noise ratio (SNR) of the received signal after alignment. For this reason, an acceptable sum rate of IA in real MIMO-orthogonal frequency-division multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the distance between network nodes and the separation between the antennas within each node to minimize the spatial correlation. In this paper, we propose to apply transmit antenna selection to MIMO-OFDM IA systems either through bulk or per-subcarrier selection, aiming at improving the sum-rate and/or error-rate performance under real-world channel circumstances, while keeping the minimum spatial antenna separation of 0.5 wavelengths within each node. Two selection criteria are considered: maximum sum rate (Max-SR) and minimum error rate (Min-ER). To avoid subcarrier imbalance across the antennas of each user, which is caused by per-subcarrier selection, a constrained per-subcarrier antenna selection is operated. Furthermore, a suboptimal antenna selection algorithm is proposed to reduce the computational complexity of the optimal algorithm. An experimental validation of MIMO-OFDM IA with antenna selection in an indoor wireless network scenario is presented. The experimental results are compared with deterministic channels that are synthesized using hybrid electromagnetic (EM) ray-tracing models. Our performance evaluation shows that the practical feasibility of MIMO-OFDM IA systems is significantly increased by antenna selection in real-world scenarios.

A distributed interference alignment algorithm using min-maxing strategy

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.

Power loading and spectral efficiency comparison of MIMO OFDM/FBMC for interference alignment based cognitive radio systems

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.

Optimal resource allocation based on interference alignment for OFDM and FBMC MIMO cognitive radio systems

Interference alignment is a joint-transmission strategy that significantly increases the sum rate of interference channels at high signal-to-noise ratios (SNRs). The recent iterative interference alignment approaches are incapable of guaranteeing the best sum-rate performance with the increase of the SNR amongst different K-user interference channels, especially at high SNR regime. In this paper, a new interference alignment algorithm is developed to improve the sum-rate performance of K-user multi-input multi-output (MIMO) and multi-carrier interference channels by minimising the interference leakage and maximising the desired power concurrently, which is called by min-maxing strategy. For a K-user MIMO interference channel, we design transmit precoding matrices and receive decoding matrices through an efficient iterative algorithm based on min-maxing strategy in a distributed way, in which each receiver maximises the desired signal power, whereas it preserves the minimum leakage interference as a constraint. This optimisation problem is reformulated and relaxed into a standard semidefinite programming form. The convergence of the proposed algorithm is proved as well. Furthermore, a simplified min-maxing algorithm is proposed for rank-deficient interference channels to achieve the targeted performance with less complexity. The numerical simulations show that the proposed algorithm proffers significant sum-rate improvement in K-user MIMO interference channels compared with the existing algorithms at high SNR regime. Moreover, the simplified algorithm matches the optimal performance in the systems of rank-deficient channels. Finally, the developed min-maxing algorithm has been extended to K-user multi-carrier interference channels, which outperform the previous approaches in terms of sum rate in several scenarios. Copyright

Min-maxing interference alignment algorithm as a semidefinite programming problem

Interference alignment (IA) has been proposed to optimally manage the interference aiming at providing the maximum degrees of freedom for multiuser interference channels. Therefore, IA has been used in cognitive radio (CR) systems to perform resource management in order to improve the throughput of the OFDM/FBMC based MIMO CR systems. In this work, a sub-optimal IA based power loading method is proposed for OFDM/FBMC based MIMO CR systems to approach the optimal approach with fewer complexity. In the proposed algorithm, all secondary users are enabled to share the available spectrum on the base of IA technique without affecting the quality-of-service of the primary system. Furthermore, spectral efficiency comparison between MIMO-OFDM and MIMO-FBMC is presented. Simulation results show that IA based power loading achieves a significant sum-rate increase of CR systems compared to traditional orthogonal multiple access techniques. Additionally, IA based power loading achieves better sum-rate improvement with FBMC than OFDM physical layer.

A novel FDD massive MIMO system based on downlink spatial channel estimation without CSIT

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.

Spatial Channel Estimation-Based FDD-MIMO Interference Alignment Systems

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.