In this work we consider the problem of downlink resource allocation for proportional fairness of long term received rates of data users and quality of service for real time sessions in an OFDMA-based wireless system. The base station allocates available power and subchannels to individual users based on long term average received rates, quality of service (QoS) based rate constraints and channel conditions. We formulate and solve a joint bandwidth and power optimization problem, solving which provides a performance improvement with respect to existing resource allocation algorithms. We propose schemes for flat as well as frequency selective fading cases. Numerical evaluation results show that the proposed method provides better QoS to voice and video sessions while providing more and fair rates to data users in comparison with existing schemes.

The recent advances in drone technology significantly improved the effectiveness of applications such as border surveillance, disaster management, seismic surveying, and precision agriculture. The use of drones as base stations to improve communication in the next generation wireless networks is another attractive application. However, the deployment of drone base stations (DBSs) is not an easy task and requires a carefully designed strategy. Fairness is one of the most important metrics of tactical communications or a disaster‐affected network and must be considered for the efficient deployment of DBSs. In this study, a fairness‐aware multiple DBS deployment algorithm is proposed. As the proposed algorithm uses particle swarm optimisation (PSO) that requires significant processing power, simpler algorithms with faster execution times are also proposed and the results are compared. The simulations are performed to evaluate the performance of the algorithms in two different network scenarios. The simulation results show that the proposed PSO‐based method finds the three‐dimensional locations of DBSs, achieving the best fairness performance with a minimum number of DBSs for deployment. However, it is shown that the proposed suboptimal algorithm performs very close to the PSO‐based solution and requires significantly less processing time.

Inspired by recent industrial efforts towards high altitude flying wireless access points powered by renewable energy, an online resource allocation problem for a mobile access point travelling at high altitude is formulated. The access point allocates its resources (available energy) to maximise the total utility (reward) provided to a sequentially observed set of users demanding service. The problem is formulated as a 0/1 dynamic knapsack problem with incremental capacity over a finite time horizon, and the solution of which is quite open in the literature. We address the problem through deterministic and stochastic formulations followed by a model where the statistics of the underlying processes are not known and learned through rule‐based and neural network approaches. For the deterministic problem, several online approximations including optimisation via genetic algorithm and rule‐based approach are proposed based on an instantaneous threshold that can adapt to short‐time‐scale dynamics. For the stochastic model, after showing the optimality of a threshold‐based solution on a dynamic programming formulation, an approximate threshold‐based policy is obtained. The performances of proposed policies are compared with that of the optimal solution obtained through dynamic programming. Copyright © 2016 John Wiley & Sons, Ltd.

As communication systems evolve to better cater to the needs of machine-type applications such as remote monitoring and networked control, advanced perspectives are required for the design of link layer protocols. The age of information (AoI) metric has firmly taken its place in the literature as a metric and tool to measure and control the data freshness demands of various applications. AoI measures the timeliness of transferred information from the point of view of the destination. In this study, we experimentally investigate AoI of multiple packet flows on a wireless multi-user link consisting of a transmitter (base station) and several receivers, implemented using software-defined radios (SDRs). We examine the performance of various scheduling policies under push-based and pull-based communication scenarios. For the push-based communication scenario, we implement age-aware scheduling policies from the literature and compare their performance with those of conventional scheduling methods. Then, we investigate the query age of information (QAoI) metric, an adaptation of the AoI concept for pull-based scenarios. We modify the former age-aware policies to propose variants that have a QAoI minimization objective. We share experimental results obtained in a simulation environment as well as on the SDR testbed.