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Abstract Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely vehicle-to-everything (V2X) and machine-type communication in future cellular networks. D2D communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to device-to-infrastructure communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, dedicated sub-granting radio resource (DSGRR), and then a distributed heuristics algorithm, open sub-granting radio resource (OSGRR), is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.

Abstract Ultra-reliable and low-latency communication (URLLC) in mobile networks is still one of the core solutions that require thorough research in 5G and beyond. With the vigorous development of various emerging URLLC technologies, resource shortages will soon occur even in mmWave cells with rich spectrum resources. As a result of the large radio resource space of mmWave, traditional real-time resource scheduling decisions can cause serious delays. Consequently, we investigate a delay minimization problem with the spectrum and power constraints in the mmWave hybrid access network. To reduce the delay caused by high load and radio resource shortage, a hybrid spectrum and power resource allocation scheme based on reinforcement learning (RL) is proposed. We compress the state space and the action space by temporarily dumping and decomposing the action. The multipath deep neural network and policy gradient method are used, respectively, as the approximater and update method of the parameterized policy. The experimental results reveal that the RL-based hybrid spectrum and the power resource allocation scheme eventually converged after a limited number of iterative learnings. Compared with other schemes, the RL-based scheme can effectively guarantee the URLLC delay constraint when the load does not exceed 130%.

Abstract High-altitude platforms (HAPs) are quasi-stationary aerial wireless communications platforms meant to be located in the stratosphere, to provide wireless communications and broadband services. They have the ability to fly on demand to temporarily or permanently serve regions with unavailable infrastructure. In this paper, we consider the development of an efficient method for resource allocation and controlling user admissions to multicast groups in a HAP system. Power, frequency, space and time domains are considered in the problem. The combination of these many aspects of the problem in multicasting over an OFDMA HAP system were not, to the best of our knowledge, addressed before. Due to the strong dependence of the total number of users that could join different multicast groups on the possible ways we may allocate resources to the different multicast groups, it is important to consider a joint user to multicast group assignments and radio resource management across the groups. From the service provider’s point of view, it would be in its best interest to be able to admit as many users as possible, while satisfying their quality of service requirements. The problem turns out to be a mixed integer non-convex non-linear program for which branch and bound solution framework is guaranteed to solve the problem. Branch and bound (BnB) can be also used to obtain sub-optimal solutions with desired quality. Even though branch and bound is guaranteed to find the optimal solution, the computational cost could be extremely high, which is why we considered different types of enhancements to BnB. Mainly, we consider reformulations by linearizing a specific set of quadratic constraints in the derived formulation, as well as the application of different branching techniques to find the one that performs the best. Based on the conducted numerical experiments, it was concluded that linearization, applied for at least 100 presolving rounds, and cloud branching achieve the best performance.

Abstract As a promising paradigm for the 5G wireless communication system, a new evolution of the cloud radio access networks has been proposed, named as fog radio access networks (F-RANs). It is an advanced socially aware mobile networking architecture to provide a high spectral and energy efficiency while reducing backhaul burden. In particular, F-RANs take full advantages of social information and edge computing to efficiently alleviate the end-to-end latency. Based on the benefit of edge and cloud processing, key issues of F-RAN technique are radio resource allocation, caching, and service admission control. In this paper, we develop a novel F-RAN system control scheme based on the embedded game model. In the proposed scheme, spectrum allocation, cache placement, and service admission algorithms are jointly designed to maximize system efficiency. By developing a new embedded game methodology, our approach can capture the dynamics of F-RAN system and effectively compromises the centralized optimality with decentralized distribution intelligence for the faster and less complex decision making process. Through simulations, we compare the performance of our scheme to the existing studies and show how we can achieve a better performance under dynamic F-RAN system environments.