A Quantum Approximate Optimization Algorithm on Conflict Graphs for Radio Resource Allocation

This paper presents a novel downlink resource allocation scheme for OFDMA-based next generation wireless networks subject to inter-cell interference (ICI). The scheme consists of radio resource and power allocations, which are implemented separately. Low-complexity heuristic algorithms are first proposed to achieve the radio resource allocation, where graph-based framework and fine physical resource block (PRB) assignment are performed to mitigate major ICI and hence improve the network performance. Given the solution of radio resource allocation, a novel distributed power allocation is then performed to optimize the performance of cell-edge users under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as an iterative barrier-constrained water-filling problem and solved by using the Lagrange method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks compared with other schemes, and therefore realize the goal of future wireless networks in terms of providing high performance to anyone from anywhere.

Mobile operators are widely adopting Network Functions Virtualization (NFV) to get the benefits of virtualization, including ease of deployment, flexibility, and cost savings. NFV allows multiple virtualized Radio Access Networks (vRANs) to run on commodity hardware enabling joint signal processing and efficient interference management. In addition, mobile operators can run general-purpose workloads alongside vRANs to utilize spare system resources in the NFV infrastructure. In such a consolidated scenario, it is necessary to ensure that the Key Performance Indicators (KPIs) of vRAN workloads are always met. But the workload consolidation could cause high variability and unpredictability in the performance of the deployed vRANs due to contentions for shared system resources like CPU cores, Last Level Cache (LLC), etc. In order to address this problem, we present JARS – a joint allocation of radio and system resources for the NFV infrastructure – that dynamically adjusts system resources such as CPU cores and LLC-ways, and radio resources such as Physical Resource Blocks (PRBs) to ensure KPIs for vRANs and improve overall resource utilization by workload consolidation. We profile srsLTE to determine the minimal CPU core and LLC resource requirements to satisfy KPIs during different traffic loads, which is used in the JARS. Experimental studies on a prototype system show that the proposed JARS outperforms a state-of-the-art scheme by 23%.

In the 5th-Generation of Mobile Networks (5G), network slices are formed to provide customized services by sharing the wireless resource and the infrastructures of the system. To improve the wireless resource capacity, it is necessary to multiplex the wireless resource among the slices efficiently. At the same time, wireless resource isolation is significant to meet the quality-of-service (QoS) requirements of one slice when other slices vary. Therefore, how to achieve high wireless resource capacity while achieving the wireless resource isolation among slices is a significant problem. This paper studies wireless resource allocation in sliced 5G networks considering the tradeoff between the multiplexing and the isolation of wireless resource. Firstly, two indicators are defined to denote the wireless resource multiplexing gain and the wireless resource isolation performance, respectively. And the relationship between them is further analyzed. Next, the wireless resource allocation is formulated as an optimization problem, which aims to maximize the wireless resource multiplexing gain with the constraint of the wireless resource isolation requirements. Then, an improved genetic algorithm based on the optimal preservation scheme and the matrix coding is designed to solve the formulated problem. Simulation results show that, compared with the existing scheme without the isolation performance constraints of the slices and the scheme without multiplexing wireless resource among slices, the proposed scheme can achieve a better resource multiplexing gain while guaranteeing the isolation requirements of the slices.

With the rapid increase of smart grid's intelligence degree, types and number of communication services are also increasing. It will be a challenge to design a resource allocation method meeting various QoS requirements and effectively using substrate resources. In this paper, a cost-and-QoS-based resource allocation mechanism is proposed to solve wireless resource slicing problem in access network. It virtualizes substrate wireless resources and builds a combined and non-convex optimization model, then uses simulated annealing optimization algorithm (SAOA) to complete allocation of virtual wireless resources. Simulation results show that compared with QoS-based wireless resource allocation algorithm (QoS-WRA) and wireless resource dynamic allocation algorithm (WRDA), SAOA has stronger advantages in saving energy consumption and ensuring transmission rate.

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, e.g., in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this paper, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep learning assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

With the objectives of improving 3G networks coverage by using relaying nodes, a game theoretic approach to radio resource allocation for the downlink capacity is introduced. One of the basic issues not examined in the literature is the importance of fair resource sharing among mobile nodes located along a multihop link. We describe a novel technique for providing a resource allocation mechanism in a multihop relaying network. The resource allocation problem is formulated as a cooperative game using Nash Bargaining Solution (NBS), which allows mobile nodes to fairly share a downlink bandwidth among themselves. Sharing of the downlink capacity between multiple nodes using a noncooperative approach is inefficient when the radio resource is scarce. If upstream nodes manipulate their location at the head of the multihop link to exploit the downlink capacity, downstream nodes will suffer disproportionately. The undesirable properties can be avoided by means of a cooperative agreement in which all nodes share the radio resources equally, where downstream nodes are allowed to pay compensation to prevent upstream nodes from exploiting the downlink capacity and encourage them to cooperate.

Device-to-Device (D2D) communications can increase the spectral efficiency of future cellular networks when sharing part of the cellular spectrum. Radio resource allocation mechanisms are then necessary to control the interference that D2D and cellular transmissions can generate to each other. Most of the existing allocation schemes rely on the knowledge of the channel gain of all possible links between cellular and D2D nodes. This paper proposes to reduce the complexity cost and signalling overhead of the allocation process by using location information available at the network level. Using this information, the base station assigns radio resources to new D2D transmissions with the objective to control and limit the interference to the primary cellular users and existing D2D transmissions. The proposed radio resource allocation scheme continuously monitors that the user QoS requirements are satisfied. If it is not the case, it dynamically modifies the resource allocation to the interfering D2D transmissions. The proposed scheme achieves performance levels similar to that obtained with an optimized centralized allocation scheme, but with a significantly lower complexity cost and signaling overhead

In the fifth generation (5G) wireless communication network, to better offer service for different vertical industry applications, network slicing is needed to meet the differential requirements. In this paper, we study the joint problem of radio resource allocation and power resource allocation in the radio access network (RAN) to improve the radio resource utilization with multi-user demands in smart grid scenario. Different with conventional single-agent algorithm, we propose a non-orthogonal multiple access (NOMA) based multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve this dynamic optimization problem. The scheme determines the remote radio unit (RRU) and physical resource block (PRB) allocation for each user, and updates the neural network to dynamically adjust the resource allocation strategy to maximize the long-term rate of network slices. The reliability and latency requirements of network slicing are guaranteed as well. Simulation results show the convergence and efficiency of the proposed algorithm.

Device-to-device (D2D) communication as an underlaying cellular network empowers user-driven rich multimedia applications and also has proven to be network efficient offloading eNodeB traffic. However, D2D transmitters may cause significant amount of interference to the primary cellular network when radio resources are shared between them. During the downlink (DL) phase, primary cell UE (user equipment) may suffer from interference by the D2D transmitter. On the other hand, the immobile eNodeB is the victim of interference by the D2D transmitter during the uplink (UL) phase when radio resources are allocated randomly. Such interference can be avoided otherwise diminish if radio resource allocated intelligently with the coordination from the eNodeB. In this paper, we formulate the problem of radio resource allocation to the D2D communications as a mixed integer nonlinear programming (MINLP). Such an optimization problem is notoriously hard to solve within fast scheduling period of the Long Term Evolution (LTE) network. We therefore propose an alternative greedy heuristic algorithm that can lessen interference to the primary cellular network utilizing channel gain information. We also perform extensive simulation to prove the efficacy of the proposed algorithm.

Mobile cellular networks are now serving all kinds of Internet of Things (IoT) communications. Since current contention-based random access and radio resource allocation are optimized for traditional human communications, massive IoT communications cannot be efficiently accommodated. For this reason, standardization activities for connecting IoT devices, such as Cellular-IoT (C-IoT), have emerged. However, there have been few studies devoted to the evaluation of the performance of the C-IoT communications with periodic data transmissions, despite their being the common characteristics of many IoT communications.Herein, we evaluate the capacity of mobile cellular networks in accommodating periodic C-IoT communications, focusing on differences in performance between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we conduct end-to-end performance analyses of both control and data planes, including the random access procedure, radio resource allocation, and bearer establishment in EPC network. Moreover, we determined the effect of immediate release of radio resources considered in 3GPP. Numerical evaluation results show that NB-IoT can accommodate more IoT devices than LTE, although this results in significant latency in data transmission. Furthermore, we find that the number of IoT devices that can be accommodated increases up to 20.7 times with immediate release of radio resources.

The Internet of Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature radio access technologies such as GSM, Long‐Term Evolution, Wi‐Fi, and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use low‐power wide‐area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a radio resource management framework, based on software‐defined network (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software‐defined radio‐based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latest IoT technologies, ie, LoRa and SigFox, operate. We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications.

The software‐defined wireless sensor networks (SDWSNs) have been proposed recently to solve the energy limitation of sensor nodes and extend the lifetime of the wireless sensor networks by fast node reconstruction and dynamical resource allocation. In this study, the authors investigate an energy‐efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity. In this algorithm, the authors formulate an optimisation problem to minimise the energy consumption, under the individual constraint of quality of service. Then, the initial optimisation problem is transformed using semidefinite relaxation, to achieve centralised adaptive bandwidth and power allocation (CABPA). Additionally, two special cases are derived to reveal the performance of the CABPA. Furthermore, an OpenFlow‐based scheme is proposed for information exchanging and updating to realise the centralised resource allocation. Meanwhile, a distributed scheme with limited information about the whole network is developed to serve as a performance benchmark for the CABPA in the SDWSN. Finally, the simulation results reveal that the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.

Allocation of radio resources is very critical to Long Term Evolution (LTE) mobile network. Therefore, it is essential to design and implement optimum scheduling schemes to satisfy the required Quality of Service (QoS) that is vital for Real Time (RT) applications. In this paper, a new scheduling strategy for supporting both RT and Non-RT (NRT) applications is presented. The proposed scheduling scheme performs the allocation process of the available radio resources at two levels. At the first level of the scheduler, an optimized delay threshold is proposed to efficiently control Resource Blocks (RBs) allocation for video traffic. At this level, all video packets are investigated, and only users with Head of Line (HoL) packets delay greater than the optimized delay threshold will be considered for the allocation process in the next level. However, at the introduced scheduler’s second level, RBs are assigned to RT packets according to HoL delays, based on an enhanced algorithm that depends on Earliest Due Date (EDD) mechanism. The behaviour of the introduced scheduling strategy is investigated and compared to the behaviour of other schedulers considered in this research. Simulation results demonstrate that the introduced two-level scheduling strategy provides the best level of quality for the supported services, especially for RT applications. It provides the lowest Packet Loss Rate (PLR) and packet delay, while achieving the highest throughput when compared to other schedulers considered in this research. In addition, the proposed two-level scheduler achieves the highest efficiency of the spectral and system capacity.

With the rapid development of NGSO satellite constellation, multi‐antenna gateway station has become a trend to increase the capacity of communication system. Since the wide use of Ku/Ka bands as well as the dynamic characteristics of NGSO networks, the co‐frequency interference between constellations is inevitable, and the interference limits become important constraints to the system capacity of multi‐antenna gateway stations. In this paper, the allocation of radio resources, including power and beam, under the interference constraints is formulated as an optimization problem. However, traditional optimization methods may not be suitable for NGSO satellite coexistence scenarios due to the fast movements of NGSO satellites. In order to address this problem, the optimization problem is transformed into two subproblems, and a Jensen's Inequality‐based Fast Iteration (JI‐FI) algorithm is designed to solve both of them iteratively. Simulation results show that the interference can be effectively reduced to the value below the threshold and beam and power resources can be allocated simultaneously by adopting the proposed algorithm. Compared with the traditional methods, the proposed scheme can significantly improve the system capacity by more than 25%, and it can greatly reduce the computational complexity.

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation.