This article focuses on the slot access problem for neighboring cooperation in unmanned aerial vehicle (UAV) swarms. To avoid the slot access process being hindered by unavailable topology information or information exchanges, a self-organized collision discovery mechanism is proposed. Each broadcaster can know whether its transmission is successful through the mechanism which provides the basic knowledge for finding the slot access strategy. Considering the distributed feature, the slot access problem is formulated as two game models. Both games are proved to have at least one Nash Equilibrum (NE) and the best NE is the optimum of the problem. Two distributed and synchronous algorithms are proposed to reach the NE. The first algorithm converges fast which satisfies the dynamic feature of UAV swarms and the second one converges to the optimum asymptotically. Moreover, to enhance the time efficiency of UAV swarms, the total number of required slots is investigated in some typical topologies and then conjectured to general ones. Simulation results verify that the proposed method is effective and the conjecture is true in almost all topologies.

Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

The distributed cooperative hybrid caching problem based on content-awareness in device-to-device networks is studied in this letter. Besides caching from the base station, nodes also can cache files from nearby nodes. We also consider the content similarity between caching nodes, which would reduce the cost further through caching similar traffic from one source cooperatively. We model the cost reducing problem as a local cooperative game, and prove it to be an exact potential game, which has at least one pure Nash equilibrium (NE). Fortunately, the potential function is just the aggregate cost of the network, which means the NE point minimizes the total cost. We modified the log-linear learning algorithm and designed a half-fixed action to reduce the strategy space, and with random action to pursue better performance. The simulation results show that the modified log-linear learning algorithm achieves better performance compared with other algorithms, and the content-aware hybrid caching reduces the cost.

Although the promising 5G cell network technology has increased the transmitting rate greatly, it has also brought some challenges. The energy efficiency has become an important topic in 5G networks. In this paper, the energy efficiency of small cell networks is analyzed, and the existing objective functions are classified in order to minimize the energy consumption, and to maximize the energy efficiency, harvested energy, and energy-aware transmission. Commonly used metrics were analyzed on equipment, base station, and network levels, respectively. Moreover, the methods for energy efficiency improvement were introduced according to above-mentioned metrics. Afterward, the relationships between energy efficiency, spectrum efficiency, and space efficiency were discussed. In order to improve efficiency on equipment, base station, and network levels, the energy and spectrum market is proposed and guidelines for the future research on metrics, methods, and market are presented. The proposed market was verified by simulations, and the simulation results have shown that the proposed market improves the energy efficiencies effectively.

Multi-access edge computing (MEC) system is developing to take advantage of the adjacent computation devices to reach better performance for IoT networks, and has to fight against malicious jamming attacks and heavy co-channel interference. To address this issue, this paper investigates the energy-efficient MEC optimization problem in a jamming environment where the attack activities of the jammer are time-varying and a priori unknown, and tries to obtain the joint channel access, offloading ratio and power solutions for IoT device-MEC server pairs. To model the interactions between users and the malicious jammer, a joint channel access and data offloading anti-jamming game is formulated. Moreover, it is proved that the proposed game is an exact potential game (EPG), and there exist at least one pure Nash Equilibrium (NE). To obtain NEs in the dynamic jamming environment, a Multi-pattern Best Response based Channel access and Data offloading (MBRCD) algorithm is designed. Through the “game learning” structure, the global network energy consumption can be greatly reduced under the premise of completing computation tasks. Furthermore, simulations indicate that the proposed approach outperforms other comparative approaches, and is well-adapted to the dynamic jamming environment.

This paper investigates the problem of almost blank subframe (ABS)-slot access in the heterogeneous networks to optimize network performance under the ABS scheme. Since maximizing network throughput requires global information and is not feasible in large scale or dense networks, the problem is solved from an interference minimization perspective after investigating the inherent relationship between local interference and achievable throughput. Since traditional binary interference model does not consider accumulated interference and thus results in inaccuracy, we take the accumulated interference into account and propose a generalized interference model, in which small cells are classified into different distant neighborhoods. An interference minimization game is formulated and is then proved to be an exact potential game, which has at least one pure Nash equilibrium (NE), and the best pure strategy NE point is a global optimum of minimizing aggregate interference. Combining the relationship between throughput and interference, the throughput maximization problem is proved to be an ordinal potential game, which possesses nice properties as well. A distributed algorithm is proposed to reach the NE, which minimizes the aggregate interference and maximizes the network throughput globally or locally. Furthermore, simulation results show that the proposed generalized interference model outperforms the traditional binary interference model.