On the Degrees-of-freedom of the 3-user MISO Broadcast Channel with Hybrid CSIT

The 3-user multiple-input single-output (MISO) broadcast channel (BC) with hybrid channel state information at the transmitter (CSIT) is considered. In this framework, there is perfect and instantaneous CSIT from a subset of users and delayed CSIT from the remaining users. We present new results on the degrees of freedom (DoF) of the 3-user MISO BC with hybrid CSIT. In particular, for the case of 2 transmit antennas, we show that with perfect CSIT from one user and delayed CSIT from the remaining two users, the optimal DoF is 5/3. For the case of 3 transmit antennas and the same hybrid CSIT setting, it is shown that a higher DoF of 9/5 is achievable and this result improves upon the best known bound. Furthermore, with 3 transmit antennas, and the hybrid CSIT setting in which there is perfect CSIT from two users and delayed CSIT from the third one, a novel scheme is presented which achieves 9/4 DoF. Our results also reveal new insights on how to utilize hybrid channel knowledge for multi-user scenarios

Retroactive Anti-Jamming for MISO Broadcast Channels

Jamming attacks can significantly impact the performance of wireless communication systems. In addition to reducing the capacity, such attacks may lead to insurmountable overhead in terms of re-transmissions and increased power consumption. In this paper, we consider the multiple-input single-output (MISO) broadcast channel (BC) in the presence of a jamming attack in which a subset of the receivers can be jammed at any given time. Further, countermeasures for mitigating the effects of such jamming attacks are presented. The effectiveness of these anti-jamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT). The main contribution of this paper is the characterization of the DoF region of the two user MISO BC under various assumptions on the availability of CSIT and JSIT. Partial extensions to the multi-user broadcast channels are also presented.Comment: submitted to IEEE Transactions on Information Theor

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

A Novel Beamformed Control Channel Design for LTE with Full Dimension-MIMO

The Full Dimension-MIMO (FD-MIMO) technology is capable of achieving huge improvements in network throughput with simultaneous connectivity of a large number of mobile wireless devices, unmanned aerial vehicles, and the Internet of Things (IoT). In FD-MIMO, with a large number of antennae at the base station and the ability to perform beamforming, the capacity of the physical downlink shared channel (PDSCH) has increased a lot. However, the current specifications of the 3rd Generation Partnership Project (3GPP) does not allow the base station to perform beamforming techniques for the physical downlink control channel (PDCCH), and hence, PDCCH has neither the capacity nor the coverage of PDSCH. Therefore, PDCCH capacity will still limit the performance of a network as it dictates the number of users that can be scheduled at a given time instant. In Release 11, 3GPP introduced enhanced PDCCH (EPDCCH) to increase the PDCCH capacity at the cost of sacrificing the PDSCH resources. The problem of enhancing the PDCCH capacity within the available control channel resources has not been addressed yet in the literature. Hence, in this paper, we propose a novel beamformed PDCCH (BF-PDCCH) design which is aligned to the 3GPP specifications and requires simple software changes at the base station. We rely on the sounding reference signals transmitted in the uplink to decide the best beam for a user and ingeniously schedule the users in PDCCH. We perform system level simulations to evaluate the performance of the proposed design and show that the proposed BF-PDCCH achieves larger network throughput when compared with the current state of art algorithms, PDCCH and EPDCCH schemes

How to choose a neural network architecture? – A modulation classification example

Which neural network architecture should be used for my problem? This is a common question that is encountered nowadays. Having searched a slew of papers that have been published over the last few years in the cross domain of machine learning and wireless communications, the authors found that several researchers working in this multi-disciplinary field continue to have the same question. In this regard, we make an attempt to provide a guide for choosing neural networks using an example application from the field of wireless communications, specifically we consider modulation classification. While deep learning was used to address modulation classification quite extensively using real world data, none of these papers give intuition about the neural network architectures that must be chosen to get good classification performance. During our study and experiments, we realized that this simple example with simple wireless channel models can be used as a reference to understand how to choose the appropriate deep learning models, specifically neural network models, based on the system model for the problem under consideration. In this paper, we provide numerical results to support the intuition that arises for various cases. © 2020 IEEE

Graph Neural Networks-Based User Pairing in Wireless Communication Systems

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs