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This paper proposes a novel cooperative amplify and forward (CAF) architecture for MIMO WLANs to improve their coverages and transmission rates, without requiring any modifications in the IEEE 802.11 standards at the clients. System models are used for analyzing physical layer of CAF networks, and compared with conventional amplify-and-forward (AF) and decode-and-forward (DF) networks. Numerical results demonstrate that CAF network clearly outperforms conventional 802.11 networks, AF and DF networks in terms of coverage and throughput. 

Unmanned aerial vehicles (UAVs) can supplement the existing ground-based heterogeneous cellular networks (Het-Nets), by replacing/supporting damaged infrastructure, providing real-time video support at the site of an emergency, offloading traffic in congested areas, extending coverage, and filling coverage gaps. In this paper, we introduce distributed algorithms that leverage UAV mobility, enhanced inter-cell interference coordination (ICIC), and cell range expansion (CRE) techniques defined in 3GPP Release-10 and 3GPP Release-11. Through Monte-Carlo simulations, we compare the system-wide 5th percentile spectral efficiency (5pSE) while optimizing the performance using a brute force algorithm, a heuristic-based sequential algorithm, and a deep Q-learning algorithm. The autonomous UAVs jointly optimize their location, ICIC parameters, and CRE to maximize 5pSE gains and minimize the outage probability. Our results show that the ICIC technique relying on a simple heuristic outperforms the ICIC technique based on deep Q-learning. Taking advantage of the multiple optimization parameters for interference coordination, the heuristic based ICIC technique can achieve 5pSE values that are reasonably close to those achieved with exhaustive brute force search techniques, at a significantly lower computational complexity.