Publications

Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing number of users are served simultaneously while providing a solution for the scarcity of the bandwidth. In this paper we present a method for clustering the users in a mmWave-NOMA system with the objective of maximizing the sum-rate. An unsupervised machine learning technique, namely, hierarchical clustering is utilized which does the automatic identification of the optimal number of clusters. The simulations prove that the proposed method can maximize the sum-rate of the system while satisfying the minimum QoS for all users without the need of the number of clusters as a prerequisite when compared to other clustering methods such as k-means clustering.

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.

This paper studies in-band interference caused by different kinds of ultra wideband signals in UMTS frequency bands as a function of UWB pulse width. UWB frequency spectra are produced by using several types of narrow pulse waveforms. Due to the extremely wide bandwidth these signals will spread over the frequency bands allocated to other RF-systems. Study revealed that one can reduce interfering UWB power by using different waveforms and pulse widths to avoid UMTS frequencies without any additional filtering. The simulations did not make a significant difference between time hopping and direct sequence concepts when interference was calculated at the UMTS bands.

Many performance evaluations for the IEEE 802.11 Distributed Coordination Function (DCF) have been previously reported in the literature; most studies are based on saturation analysis, and a few models under a finite-load condition adopt an M/G/1 queuing system. However, using M/G/1 queuing only considers the first moment of the frame service time to derive the probability of the transmission queue being vacant. In this paper, we model the DCF using the parallel space-time Markov chain, where the frame arrivals are tracked by monitoring the transmission queue during transitions between successive states of the space-time Markov chain. The proposed framework provides the possibility of simultaneously modeling the contention phase, the backoff and post-backoff procedures, and the transmission queue status. The proposed framework is also validated by simulation results.

No abstract is provided for this article.

The femtocell concept is an emerging technology for deploying the next generation of the wireless networks, aiming at indoor coverage enhancement, increasing capacity and offloading the overlay macrocell traffic. One of the main challenges in short range femtocell networks is how to (re)configure the Home Node Bs (HNBs) in an autonomous manner so as to manage interference and diminish the energy consumption among nearby femtocells efficiently. In this paper, we investigate the indoor femtocell deployment making use of both the Frequency Division Duplexing (FDD) and the Time Division Duplexing (TDD) methods. In the FDD case, the HNBs share both the Uplink (UL) and Downlink (DL) channels with the macrocell without any cooperation to coordinate their access to the air interface. Conversely, in the TDD underlay case, femtocells only reuse the macrocell UL spectrum and cooperate with each other in order to minimize the interference among themselves, either with or without further coordination with the Macro User Equipment (MUE). The proposed solution is evaluated by means of system-level simulations using the Monte Carlo approach. Investigations have shown that the TDD underlay approach not only reduces the perceived interference levels, but also diminishes the outage probability.

This paper investigates the performance of a Full-Duplex Block Markov (FD-BM) relaying scheme with self interference at the relay under independent non-identically distributed Nakagami-m fading. Relying on efficient approximations proposed elsewhere in the literature for the sum of Gamma random variables (RVs), and based on a new, accurate approximation for the product of two Nakagami-m RVs, tight closed-form approximate expressions for the outage probability and throughput are derived. Representative numerical results show the accuracy of the proposed approximations.

This letter investigates the problem of providing gigabit wireless access with reliable communication in 5G millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) networks. In contrast to the classical network design based on average metrics, a distributed risk-sensitive reinforcement learning-based framework is proposed to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km2, the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.

This paper considers an optimal single stream beamforming for a multiple-input multiple-output (MIMO) relay network with non-coherent dual-hop amplify-and-forward (AF) relaying. The source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. The channel state information is only available at the destination, and the destination computes the optimal transmit and receive beamforming vectors to maximize the instantaneous signal-to-noise ratio (SNR) at the destination. The optimal transmit beamforming vector will be sent back to the transmitter via a dedicated feedback link. We derive closed-form expressions for the cumulative density function, probability density function, and moments to statistically characterize the properties of the instantaneous SNR. These statistical properties are used to analyze the performance of the system with outage probability, average bit error rate, and ergodic capacity. The results of the performance analysis reveal that Rician factor and number of antennas at the nodes improve the system performance, and the optimal single stream beamforming system provides better error performance than an orthogonal space-time block coded based AF MIMO system.

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

We introduce two mechanisms for interference mitigation, inspired by evolutionary game theory and machine learning to support the coexistence of a macrocell network underlaid with self-organized femtocell networks. In the first approach, stand-alone femtocells choose their strategies, observe the behavior of other players, and make the best decision based on their instantaneous payoff, as well as the average payoff of all other femtocells. We formulate the interactions among selfish femtocells using evolutionary games and demonstrate how the system converges to an equilibrium. In contrast, in the Reinforcement-Learning (RL) approach, information exchange among femtocells is no longer possible and hence each femtocell adapts its strategy and gradually learns by interacting with its environment (i.e., neighboring interferers) through trials-and-errors. Our investigations reveal that through learning, femtocells are able to self-organize by relying only on local information, while mitigating the interference towards the macrocell network. Besides, a trade-off exists where faster convergence is obtained in the evolutionary case as compared to the RL approach, at the expense of more side information. Finally, it is shown that femtocells face an interesting tradeoff of exploration versus exploitation in their learning processes.

The 5G CHAMPION Consortium will provide the first fully integrated and operational 5G prototype in 2018 - this effort is a major leap ahead compared to existing punctual technology trials, such as, e.g., Proof-of-Concept platforms focusing on mmWave communication in specific bands, etc. This paper describes the overall set-up including a synergetic combination of technologies such as beamforming based mmWave & Satellite service provisioning, virtualized infrastructure, software reconfiguration across the entire stack, accurate positioning and high-speed solutions. The key enablers are described in detail and related efforts in standards and regulation organizations are discussed.

This paper considers linear downlink transceiver design for the sum power minimization problem with per-user rate constraints in a multi-cell multi-user MIMO system. This non-convex problem is divided into transmit and receive beamforming optimization steps which are iteratively repeated such that the sum power converges. The transmit beamformers are solved using a successive convex approximation method (SCA), whereas the receive beamformers are computed via the linear minimum mean square error (MMSE) criterion. In addition to centralized design, we propose two decentralized algorithms where the transmit and receive beamforming designs are facilitated by a combination of pilot and backhaul signaling. In the first algorithm, sum power is minimized by computing the transmit beamformers at each base station (BS) and the receive beamformers at each user via iterative over-the-air optimization process. BS side processing requires local effective channel state information (CSI) acquired from precoded uplink pilots. In the second algorithm, the transmit and receive beamformers are iteratively optimized at each BS requiring only local CSI obtained from antenna-specific uplink pilots. In both algorithms, transmit beamforming optimization is decoupled among the BSs via primal decomposition method, which requires scalar backhaul information exchange for decentralized processing. The performance of the proposed algorithms is evaluated via numerical examples under different system settings in static and time-correlated channel conditions.