In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and flexible deployment. This paper introduces a framework designed to optimize the cooperation between aerial and terrestrial networks, with the goal of maximizing the deployment cost efficiency (DCE) of the network (i.e., the ratio of the network's total data transmission rate to the combined deployment and energy costs). The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we formulate a problem focused on maximizing the DCE while adhering to power constraints and zero intra-cell pilot contamination. Subsequently, we propose a pilot-contamination aware user clustering, and a distributed coalition formation game for BSs and UAVs clustering in C-CF-M-MIMO-enabled IATN. Our numerical findings demonstrate the efficacy of the proposed algorithm when compared to conventional benchmark methods. Furthermore, the C-CF-M-MIMO-enabled IATN outperforms BSs-only and UAVs-only network equipped with typical cell-free configurations, such as (i) traditional CF-MIMO and (ii) user-centric CF-MIMO.

To enable massive connectivity and connecting the unconnected, aerial communications are becoming critical to complement with the terrestrial infrastructure. Integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and deployment flexibility. This paper presents a framework to optimize the deployment of aerial network and cooperation among aerial-terrestrial network such that the network deployment cost efficiency (i.e. the ratio of network sum-rate and deployment-plus-energy-cost) is maximized. The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we first formulate a Deployment Cost Efficiency (DCE) maximization problem subject to power budget, zero intra-cell pilot contamination, and UAV location constraints. We then propose a grid-based joint UAV density and location optimization, a pilot-contamination aware user clustering, and a distributed coalition game approach for clustering in C-CF-M-MIMO-enabled IATN. Complexity and convergence of the proposed algorithm are presented. Our numerical results show the efficacy of the proposed algorithm compared to conventional benchmarks. The proposed C-CF-M-MIMO-enabled IATN also outperforms terrestrial-only and aerial-only networks enabled with typical cell-free configurations, namely, (i) traditional CF-MIMO, and (ii) user-centric CF-MIMO.

We study the resource sharing problem in a drone-based wireless network by considering a distributed control setting under uncertainty (e.g., due to lack of full information). The drones cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other drones. Compared to the state-of-the-art research in drone-based wireless networks, which is mainly based on the assumption of accurate global information availability at every drone, our setting is realistic and practical. We cast the efficient resource pooling problem as a Bayesian cooperative game in which the agents (drones) engage in a coalition formation process, where the goal is to maximize the overall transmission rate of the network. The drones update their beliefs by using a novel technique that combines the maximum likelihood estimation with Kullback-Leibler divergence. We propose a decision-making strategy for repeated coalition formation that converges to a stable coalition structure. We analyze the performance of the proposed approach by both theoretical analysis and simulations. We provide the comparison of our scheme with the baseline and the socially optimal solution obtained from the exhaustive search. Simulation results demonstrate the superior performance of the proposed method in terms of the sum-rate of the network, the individual rate of the drones, and convergence properties.

Devices like smart phones come with 3G/4G and WiFi radios, which creates possibilities of heterogeneous network access. We investigate scenarios where Internet access to a device is available only via the cellular network. However, not every user may connect directly to it. Users in the network may be split into hotspots and clients. Hotspots are the users that connect directly to the cellular network and may provide connectivity to the internet to other users by allowing them to connect to their WiFi interface. Clients connect to the cellular network only via hotspots. The optimization problem is to find the split of hotspots and clients, and the association between clients and hotspots, that maximizes the sum of the link rates of users. Importantly, the users must get at least the link rate they get when all are directly connected to the cellular network. In this paper, we formulate the optimization problem. We provide insights into the interplay of WiFi connectivity amongst users, their link rates to the cell tower, and the split that maximizes sum rate. We propose a novel heuristic approach to split the network. Median gains of 1.5× are observed over networks of up to 40 nodes.