Elements of Cellular Blind Interference Alignment --- Aligned Frequency Reuse, Wireless Index Coding and Interference Diversity

We explore degrees of freedom (DoF) characterizations of partially connected wireless networks, especially cellular networks, with no channel state information at the transmitters. Specifically, we introduce three fundamental elements --- aligned frequency reuse, wireless index coding and interference diversity --- through a series of examples, focusing first on infinite regular arrays, then on finite clusters with arbitrary connectivity and message sets, and finally on heterogeneous settings with asymmetric multiple antenna configurations. Aligned frequency reuse refers to the optimality of orthogonal resource allocations in many cases, but according to unconventional reuse patterns that are guided by interference alignment principles. Wireless index coding highlights both the intimate connection between the index coding problem and cellular blind interference alignment, as well as the added complexity inherent to wireless settings. Interference diversity refers to the observation that in a wireless network each receiver experiences a different set of interferers, and depending on the actions of its own set of interferers, the interference-free signal space at each receiver fluctuates differently from other receivers, creating opportunities for robust applications of blind interference alignment principles

Interference Management in Heterogeneous Networks with Blind Transmitters

Future multi-tier communication networks will require enhanced network capacity and reduced overhead. In the absence of Channel State Information (CSI) at the transmitters, Blind Interference Alignment (BIA) and Topological Interference Management (TIM) can achieve optimal Degrees of Freedom (DoF), minimising network's overhead. In addition, Non-Orthogonal Multiple Access (NOMA) can increase the sum rate of the network, compared to orthogonal radio access techniques currently adopted by 4G networks. Our contribution is two interference management schemes, BIA and a hybrid TIM-NOMA scheme, employed in heterogeneous networks by applying user-pairing and Kronecker Product representation. BIA manages inter- and intra-cell interference by antenna selection and appropriate message scheduling. The hybrid scheme manages intra-cell interference based on NOMA and inter-cell interference based on TIM. We show that both schemes achieve at least double the rate of TDMA. The hybrid scheme always outperforms TDMA and BIA in terms of Degrees of Freedom (DoF). Comparing the two proposed schemes, BIA achieves more DoF than TDMA under certain restrictions, and provides better Bit-Error-Rate (BER) and sum rate performance to macrocell users, whereas the hybrid scheme improves the performance of femtocell users.Comment: 30 pages, 18 figure

Maximum-rate Transmission with Improved Diversity Gain for Interference Networks

Interference alignment (IA) was shown effective for interference management to improve transmission rate in terms of the degree of freedom (DoF) gain. On the other hand, orthogonal space-time block codes (STBCs) were widely used in point-to-point multi-antenna channels to enhance transmission reliability in terms of the diversity gain. In this paper, we connect these two ideas, i.e., IA and space-time block coding, to improve the designs of alignment precoders for multi-user networks. Specifically, we consider the use of Alamouti codes for IA because of its rate-one transmission and achievability of full diversity in point-to-point systems. The Alamouti codes protect the desired link by introducing orthogonality between the two symbols in one Alamouti codeword, and create alignment at the interfering receiver. We show that the proposed alignment methods can maintain the maximum DoF gain and improve the ergodic mutual information in the long-term regime, while increasing the diversity gain to 2 in the short-term regime. The presented examples of interference networks have two antennas at each node and include the two-user X channel, the interferring multi-access channel (IMAC), and the interferring broadcast channel (IBC).Comment: submitted to IEEE Transactions on Information Theor

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work