arXiv subjects

Doha Hamza

Publications and source records attributed to Doha Hamza.

4 recordsLinked to original sources

arXiv2021

Multi-sided Matching for the Association of Space-Air-Ground Integrated Systems

Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to rural regions. However, the complex, multi-layered, and heterogeneous nature of SAGINs demands an innovative approach to designing their multi-tier associations. In this context, we propose a modeling of the SAGINs association problem using multi-sided matching theory. Our objective is to devise a reliable, asynchronous, and fully distributed approach that associates nodes across the layers to maximize the total end-to-end rate of the assigned agents. To achieve this, our problem is formulated as a multi-sided many-to-one matching game. We introduce a randomized matching algorithm with minimal information exchange. The algorithm is shown to reach an efficient and stable association between nodes in adjacent layers. Simulation results show that our proposed approach yields significant gains compared to both greedy and distance-based algorithms.

cs.NI↗

arXiv2020

The Optimal and the Greedy: Drone Association and Positioning Schemes for Internet of UAVs

This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined area to serve a number of ground users. Due to the heterogeneous nature of the network,the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum-rate of the users. The agents utility in the potential games is their marginal contribution to the global welfare or their so-called wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the user-UAV association and 3D-location problem. Next, we exploit the sub-modular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a guaranteed performance of $1-1/e$ of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few number of iterations.

eess.SP↗

arXiv2016

BLMA: A Blind Matching Algorithm with Application to Cognitive Radio Networks

We consider a two-sided matching problem with a defined notion of pairwise stability. We propose a distributed blind matching algorithm (BLMA) to solve the problem. We prove the solution produced by BLMA will converge to an $\epsilon$-pairwise stable outcome with probability one. We then consider a matching problem in cognitive radio networks. Secondary users (SUs) are allowed access time to the spectrum belonging to the primary users (PUs) provided that they relay primary messages. We propose a realization of the BLMA to produce an $\epsilon$-pairwise stable solution assuming quasi-convex and quasi-concave utilities. In the case of more general utility forms, we show another BLMA realization to provide a stable solution. Furthermore, we propose negotiation mechanism to bias the algorithm towards one side of the market. We use this mechanism to protect the exclusive rights of the PUs to the spectrum. In all such implementations of the BLMA, we impose a limited information exchange in the network so that agents can only calculate their own utilities, but no information is available about the utilities of any other users in the network.

cs.GT↗

arXiv2009

Cognitive Power Control Under Correlated Fading and Primary-Link CSI

We consider the cognitive power control problem of maximizing the secondary throughput under an outage probability constraint on a constant-power constant-rate primary link. We assume a temporally correlated primary channel with two types of feedback: perfect delayed channel state information (CSI) and one-bit automatic repeat request (ARQ). We use channel correlation to enhance the primary and secondary throughput via exploiting the CSI feedback to predict the future primary channel gain. We provide a numerical solution for the power control optimization problem under delayed CSI. In order to make the solution tractable under ARQ-CSI, we re-formulate the cognitive power control problem as the maximization of the instantaneous weighted sum of primary and secondary throughput. We propose a greedy ARQ-CSI algorithm that is shown to achieve an average throughput comparable to that attained under the delayed-CSI algorithm, which we solve optimally.

cs.IT↗