On the Coverage of Cognitive mmWave Networks with Directional Sensing and Communication

Millimeter-waves' propagation characteristics create prospects for spatial and temporal spectrum sharing in a variety of contexts, including cognitive spectrum sharing (CSS). However, CSS along with omnidirectional sensing, is not efficient at mmWave frequencies due to their directional nature of transmission, as this limits secondary networks' ability to access the spectrum. This inspired us to create an analytical approach using stochastic geometry to examine the implications of directional cognitive sensing in mmWave networks. We explore a scenario where multiple secondary transmitter-receiver pairs coexist with a primary transmitter-receiver pair, forming a cognitive network. The positions of the secondary transmitters are modelled using a homogeneous Poisson point process (PPP) with corresponding secondary receivers located around them. A threshold on directional transmission is imposed on each secondary transmitter in order to limit its interference at the primary receiver. We derive the medium-access-probability of a secondary user along with the fraction of the secondary transmitters active at a time-instant. To understand cognition's feasibility, we derive the coverage probabilities of primary and secondary links. We provide various design insights via numerical results. For example, we investigate the interference-threshold's optimal value while ensuring coverage for both links and its dependence on various parameters. We find that directionality improves both links' performance as a key factor. Further, allowing location-aware secondary directionality can help achieve similar coverage for all secondary links.

Multi-Task Learning for Multi-User CSI Feedback

Deep learning-based massive MIMO CSI feedback has received a lot of attention in recent years. Now, there exists a plethora of CSI feedback models mostly based on auto-encoders (AE) architecture with an encoder network at the user equipment (UE) and a decoder network at the gNB (base station). However, these models are trained for a single user in a single-channel scenario, making them ineffective in multi-user scenarios with varying channels and varying encoder models across the users. In this work, we address this problem by exploiting the techniques of multi-task learning (MTL) in the context of massive MIMO CSI feedback. In particular, we propose methods to jointly train the existing models in a multi-user setting while increasing the performance of some of the constituent models. For example, through our proposed methods, CSINet when trained along with STNet has seen a $39\%$ increase in performance while increasing the sum rate of the system by $0.07bps/Hz$.

System-Level Modelling and Beamforming Design for RIS-assisted Cellular Systems

Reconfigurable intelligent surface (RIS) is considered as key technology for improving the coverage and network capacity of the next-generation cellular systems. By changing the phase shifters at RIS, the effective channel between the base station and user can be reconfigured to enhance the network capacity and coverage. However, the selection of phase shifters at RIS has a significant impact on the achievable gains. In this letter, we propose a beamforming design for the RIS-assisted cellular systems. We then present in detail the system-level modelling and formulate a 3-dimension channel model between the base station, RIS, and user, to carry out system-level evaluations. We evaluate the proposed beamforming design in the presence of ideal and discrete phase shifters at RIS and show that the proposed design achieves significant improvements as compared to the state-of-the-art algorithms.

Jamming Bandits

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a novel multi-armed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that our learning algorithm converges to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sub-linear, i.e. the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.