In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

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A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilized by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific, and may vary depending on the nature of the sharing players, the level of sharing and spectrum access scheme. In this survey, we study the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing of licensed bands. Finally, we conclude this survey with open research challenges and suggest some future research directions.

Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook

This paper studies beamforming techniques for energy efficiency maximization (EEmax) in multiuser multiple-input single-output (MISO) downlink system. For this challenging nonconvex problem, we first derive an optimal solution using branch-and-reduce-and-bound (BRB) approach. We also propose two low-complexity approximate designs. The first one uses the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference so that the EEmax problem reduces to a concave-convex fractional program. Particularly, the problem is then efficiently solved by closed-form expressions in combination with the Dinkelbach's approach. In the second design, we aim at finding a stationary point using the sequential convex approximation (SCA) method. By proper transformations, we arrive at a fast converging iterative algorithm where a convex program is solved in each iteration. We further show that the problem in each iteration can also be approximated as a second-order cone program (SOCP), allowing for exploiting computationally efficient state-of-the-art SOCP solvers. Numerical experiments demonstrate that the second design converges quickly and achieves a near-optimal performance. To further increase the energy efficiency, we also consider the joint beamforming and antenna selection (JBAS) problem for which two designs are proposed. In the first approach, we capitalize on the perspective reformulation in combination with continuous relaxation to solve the JBAS problem. In the second one, sparsity-inducing regularization is introduced to approximate the JBAS problem, which is then solved by the SCA method. Numerical results show that joint beamforming and antenna selection offers significant energy efficiency improvement for large numbers of transmit antennas.

Optimal Energy-Efficient Transmit Beamforming for Multi-User MISO Downlink

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

Access Control and Resource Allocation for M2M Communications in Industrial Automation

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

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A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading

The problem of spectrum sharing between two operators in a dynamic network is considered. We allow both operators to share (a fraction of) their licensed spectrum band with each other by forming a common spectrum band. The objective is to maximize the gain in profits of both operators by sharing their licensed spectrum bands rather than using them exclusively, while considering the fairness among the operators. This is modeled as a two-person bargaining problem, and cast as a stochastic optimization. To solve this problem, we propose centralized and distributed dynamic control algorithms. At each time slot, the proposed algorithms perform the following tasks: 1) determine spectrum price for the operators; 2) make flow control decisions of users data; and 3) jointly allocate spectrum band to the operators and design transmit beamformers, which is known as resource allocation (RA). Since the RA problem is NP-hard, we have to rely on sequential convex programming to approximate its solution. To derive the distributed algorithm, we use alternating direction method of multipliers for solving the RA problem. Numerically, we show that the proposed distributed algorithm achieves almost the same performance as the centralized one. Furthermore, the results show that there is a trade-off between the achieved profits of the operators and the network congestion.

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Dynamic Inter-Operator Spectrum Sharing via Lyapunov Optimization

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.

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Deep Learning-based Power Control for Cell-Free Massive MIMO Networks

A new system model is introduced for spectrum sharing in a multi-operator environment. The infrastructure based cognitive radio network operates in underlay/overlay (hybrid) transmission mode sharing the spectrum of licensed users. Primary user networks (PUNs) are benefitted by the relaying capability of the cognitive radio base station so that their service degraded users due to low coverage, can enhance the performance. The cognitive radio network (CRN) also helps itself by transmitting its own data along with the primary data on the same band, while agreeing to maintain the signal to interference plus noise ratios of primary users' at a predetermined level. CRN operates in an underlay mode with an imposed interference threshold. In addition, maximizing the secondary user capacity by strategically changing the available frequencies from time slot to time slot is discussed with the introduction of two primary data relaying schemes (asynchronous / synchronous). The objective of the secondary user capacity maximization is reformulated into a linear programming problem with interference cancellation techniques. Power allocation for all the active users and the capacity of the secondary user is analyzed in both of the schemes

Overlay/Underlay Spectrum Sharing for Multi-Operator Environment in Cognitive Radio Networks

The fifth generation (5G) networks are expected to deliver high quality services to human and machine type users in specific locations. The recently proposed micro-operator concept aims at efficient and reliable local service delivery to complement traditional mobile network operators (MNOs) whose main objective has been to provide country-wide coverage. The uptake of 5G micro-operators requires local spectrum availability. In this paper, we propose a general framework for spectrum authorization and apply it in the recent micro-operator concept to develop local spectrum micro licensing model. To do this, we first identify the elements of a spectrum authorization model, and derive their interrelations to form a general framework for spectrum authorization. Then, we analyze the elements of the state-of-the-art spectrum authorization models including case examples of exclusive licensing, three-tier model for providing citizens broadband radio services (CBRS), and license-exempt model. Using the framework and the state-of-the-art models, we then propose and characterize the elements of the new micro licensing model to establish local 5G networks. Furthermore, our comprehensive investigation on the new micro licensing model supports to establish the micro-operator concept as a candidate solution for delivering localized 5G services. The proposed framework and its application to 5G micro-operators help to promote new market entry, encourage competition and innovation, and incentivize investment by facilitating the regulator to efficiently allocate 5G bands to the stakeholders.

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Framework for spectrum authorization elements and its application to 5G micro-operators

Recently, reconfigurable intelligent surfaces (RISs) have been identified as one potential solution to avoid performance degradation of using millimeter wave (mmWave) frequencies in vehicular communications. In this paper, we investigate the use of an RIS in a mmWave vehicular communication network. The problem of weighted sum-rate maximization in the uplink is considered, where an RIS is used to assist the communication. We focus on both single-user and multi-user cases. Single user case is solved using successive refinement algorithm, where two phase-optimization schemes that help reducing the channel estimation overhead are considered. In multi-user case, fractional programming technique is used to reformulate the original problem into a more convenient form, and an algorithm based on alternating optimization is proposed. The validity of the proposed methods is confirmed by numerical simulations. A significant performance increase is seen when utilizing an RIS in both cases. Considered phase optimization schemes are shown to result in a significant reduction in channel estimation at a cost of small performance degradation compared to the full channel state information beamforming scenario. We perform simulations to investigate the effects of mobility, and the results demonstrate the ability of an RIS to mitigate the effects of mobility to some extent. Furthermore, to get practical insights into vehicular communications aided by an RIS, we use a commercial ray-tracing tool to evaluate the performance.

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K. B. Shashika Manosha

Papers

Dynamic Inter-Operator Spectrum Sharing via Lyapunov Optimization

Deep Learning-based Power Control for Cell-Free Massive MIMO Networks

Overlay/Underlay Spectrum Sharing for Multi-Operator Environment in Cognitive Radio Networks

Framework for spectrum authorization elements and its application to 5G micro-operators

Weighted-Sum-Rate Maximization for an Reconfigurable Intelligent Surface Aided Vehicular Network