In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macrocell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones, and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs’ positions.

In this paper, we develop a unified theoretical framework for analyzing the outage performance of reconfigurable intelligent surfaces (RISs)-assisted communication systems over generalized fading channels and in the presence of phase noise. Fox’s H function theory is then utilized to derive the outage probability for various channel fading and phase noise distributions in closed-form. We further conduct an asymptotic outage analysis to obtain insightful findings. In particular, we present the maximum diversity order achievable over such channels and demonstrate the performance variation in comparison to conventional Rayleigh channels. Then, based on upper bounds and lower bounds, we propose a design criteria for RISs to achieve the maximum diversity order in the presence of phase noise. More specifically, we show that if the absolute difference between pairs of phase errors is less than $\pi /2$ , RIS-assisted communications achieve the full diversity order over independent fading channels, even in the presence of phase noise. The theoretical frameworks and findings are validated with the aid of Monte Carlo simulations.

This letter provides a unified performance analysis of secure reconfigurable intelligent surface (RIS)-assisted communications in the presence of discrete phase shifts. In particular, we derive exact secrecy outage probability in the presence of non-colluding and colluding eavesdroppers, and we obtain the corresponding diversity orders. Moreover, analytical expressions of the average secrecy rate and different scaling laws with sufficiently large signal-to-noise ratio and number of RIS reconfigurable elements are derived. Simulation results corroborate our theoretical analysis.

Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this paper, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with Integer Linear Programming. Due to its NP-hardness, we propose a low-complex Particle-Swarm-Optimization based meta-heuristic and a Greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior / mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources’ availability models, conducted simulations demonstrate that the PSO-based Algorithm achieves near-optimal results. Whereas, the Greedy Algorithm realizes only 10%-30% less success ratio than the optimal solution with negligible execution time.

Exploiting the advantages of both non-orthogonal multiple access technique and millimeter-wave communications requires joint efficient resource allocation techniques toward satisfying the stringent requirements of future mobile communication systems. This paper focuses on a multi-band (i.e., millimeter-wave band and sub-6 GHz band) wireless network where both orthogonal and non-orthogonal multiple access techniques coexist. A joint optimization of user association, transmit power allocation, sub-channel assignment, and multiple access technique selection is investigated to maximize the down-link sum-rate under a minimum rate requirement per user and power constraints. The problem is formulated as a non-convex mixed-integer optimization problem; then, it is proved to be NP -hard. First, simple greedy and meta-heuristic solutions are proposed. Then, since model-based approaches have generally a high computational complexity, model-free centralized and distributed approaches based on deep reinforcement learning technique are proposed. The latter are based on multiple parallel deep neural networks to generate resource allocation solutions. The proposed approaches are evaluated and compared. Simulation results corroborate the high performance offered by the proposed solutions for stationary and mobile users. They also highlight the benefits of employing hybrid orthogonal and non-orthogonal multiple access scheme in multi-band systems in terms of down-link sum-rate and user fairness.

The study of radio signal propagation path loss (PL) is important for planning, designing and evaluating the performance of radio communication networks. However, the state-of-the-art in PL modelling for fixed wireless networks in rural environments is still ill-equipped for making accurate predictions. This paper explores the application of the log distance PL model to heterogeneous fixed wireless networks in harsh rural propagation conditions. This model is then extended and optimized to improve its accuracy. In particular, the dataset is classified according to many criteria, radio links are split into many intervals according to their distances, antenna heights and elevations are integrated into its formula and long-term extreme seasonal variations are considered. Our study uses a wide set of measurements from the fixed wireless networks of a wireless internet service provider in rural regions of Canada. The proposed modifications improve the accuracy by 7 to 15 dB in terms of the root mean squared error.