Unmanned aerial vehicle (UAV) base stations (BSs) can be a promising solution to provide connectivity and quality-of-service guarantees during temporary events and after disasters. In this paper, we consider a scenario where UAV-BSs are serving a large number of mobile users in a hot spot area (e.g., in a stadium). We introduce non-orthogonal multiple-access (NOMA) transmission at UAV-BSs to serve more users simultaneously considering user distances as the available feedback for a user ordering during NOMA formulation. With millimeter-wave transmission and multi-antenna techniques, we assume that UAV-BS generates directional beams and multiple users are served simultaneously within the same beam. However, due to the limitations of physical vertical beamwidth of the UAV-BS beam, it may not be possible to cover the entire user region at UAV altitudes of practical relevance. During such situations, a beam scanning approach is proposed to maximize the achievable sum rates. We develop a comprehensive framework over which outage probabilities and respective sum rates are derived rigorously and we investigate the optimal operational altitude of UAV-BS to maximize the sum rates using our analytical framework. Our analysis shows that NOMA with distance feedback can provide better outage sum rates than orthogonal multiple access.

The overheads associated with feedback-based channel acquisition can greatly compromise the achievable rates of FDD based massive MIMO systems. Indeed, downlink (DL) training and uplink (UL) feedback overheads scale linearly with the number of base station (BS) antennas, in sharp contrast to TDD-based massive MIMO, where a single UL pilot trains the whole BS array. In this work, we propose a graph-theoretic approach to reducing DL training and UL feedback overheads in FDD massive MIMO systems. In particular, we consider a single-cell scenario involving a single BS with a massive antenna array serving to single-antenna mobile stations (MSs) in the DL. We assume the BS employs two-stage beamforming in the DL, comprising DFT pre-beamforming followed by MU-MIMO precoding. The proposed graph-theoretic approach exploits knowledge of the angular spectra of the BS-MS channels to construct DL training protocols with reduced overheads. Simulation results reveal that the proposed training-resources allocation method can provide approximately 35% sum-rate performance gain compared to conventional orthogonal training. Our analysis also sheds light into the impact of overhead reduction on channel estimation quality, and, in turn, achievable rates.

We consider a single-cell scenario involving a single base station (BS) with a massive array serving multi-antenna terminals in the downlink of a mmWave channel. We present a class of multiuser MIMO schemes, which rely on uplink training from the user terminals, and on uplink/downlink channel reciprocity. The BS employs virtual sector-based processing according to which, user-channel estimation and data transmission are performed in parallel over non-overlapping angular sectors. The uplink training schemes we consider are non-orthogonal, that is, we allow multiple users to transmit pilots on the same pilot dimension (thereby potentially interfering with one another). Elementary processing allows each sector to determine the subset of user channels that can be resolved on the sector (effectively pilot contamination free) and, thus, the subset of users that can be served by the sector. This allows resolving multiple users on the same pilot dimension at different sectors, thereby increasing the overall multiplexing gains of the system. Our analysis and simulations reveal that, by using appropriately designed directional training beams at the user terminals, the sector-based transmission schemes we present can yield substantial spatial multiplexing and ergodic user-rates improvements with respect to their orthogonal-training counterparts.

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) to deliver broadband wireless connectivity during temporary events, at hotspot areas, or after disasters that may destroy existing communication infrastructure. Since UAV- BSs are low power nodes, their efficient placement is important to reap the maximum capacity and coverage benefits from their deployments. By making use of \emph{UAV mobility} and multi- antenna arrays, it is possible to achieve angular domain user separation with lower feedback requirement. In this paper, we propose an approach to identify optimum hovering locations for UAV-BSs equipped with multi-antenna arrays. We formulate the problem as a signal-to-noise ratio (SNR) maximization at ground nodes subject to a constraint that interference leakage is lower than a threshold. We consider a scenario with two UAVs each with a single user attached and develop our proposed scheme to achieve: 1) angular domain user separation, and 2) SNR maximization at each user. The performance evaluation of the proposed scheme is carried out through simulations, which show that under interference limited conditions the proposed scheme provides capacity performance better than linear zero-force-beamforming (LZFBF), without the requirement of full channel state information of all the users.

In order to coexist with the WiFi systems in the unlicensed spectrum, Long Term Evolution (LTE) networks can utilize periodically configured transmission gaps. In this paper, considering a time division duplex (TDD)-LTE system, we propose a Q-Learning based dynamic duty cycle selection technique for configuring LTE transmission gaps, so that a satisfactory throughput is maintained both for LTE and WiFi systems. By explicitly taking the impact of IEEE 802.11n beacon transmission mechanism into account, we evaluate the coexistence performance of WiFi and LTE using the proposed technique. Simulation results show that the proposed approach can enhance the overall capacity performance by 19% and WiFi capacity performance by 77%, hence enabling effective coexistence of LTE and WiFi systems in the unlicensed band.