Hybrid Spectrum Sharing in mmWave Cellular Networks

While spectrum at millimeter wave (mmWave) frequencies is less scarce than at traditional frequencies below 6 GHz, still it is not unlimited, in particular if we consider the requirements from other services using the same band and the need to license mmWave bands to multiple mobile operators. Therefore, an efficient spectrum access scheme is critical to harvest the maximum benefit from emerging mmWave technologies. In this paper, we introduce a new hybrid spectrum access scheme for mmWave networks, where data is aggregated through two mmWave carriers with different characteristics. In particular, we consider the case of a hybrid spectrum scheme between a mmWave band with exclusive access and a mmWave band where spectrum is pooled between multiple operators. To the best of our knowledge, this is the first study proposing hybrid spectrum access for mmWave networks and providing a quantitative assessment of its benefits. Our results show that this approach provides major advantages with respect to traditional fully licensed or fully unlicensed spectrum access schemes, though further work is needed to achieve a more complete understanding of both technical and non technical implications

Stable Matching based Resource Allocation for Service Provider\u27s Revenue Maximization in 5G Networks

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled service providers (SPs) and service requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs\u27 revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching-based solution. Moreover, we offer an adaptive stable matching based distributed algorithm to solve the formulated problem in a dynamic network model. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieves 94 percent of optimum network performance

Inter-micro-operator interference protection in dynamic TDD system

Abstract. This thesis considers the problem of weighted sum-rate maximization (WSRM) for a system of micro-operators subject to inter-micro-operator interference constraints with dynamic time division duplexing. The WSRM problem is non-convex and non-deterministic polynomial hard. Furthermore, micro-operators require minimum coordination among themselves making the inter-micro-operator interference management very challenging. In this regard, we propose two decentralized precoder design algorithm based on over-the-air bi-directional signalling strategy. We first propose a precoder design algorithm by considering the equivalent weighted minimum mean-squared error minimization reformulation of the WSRM problem. Later we propose precoder design algorithm by considering the weighted sum mean-squared error reformulation. In both approaches, to reduce the huge signalling requirements in centralized design, we use alternating direction method of multipliers technique, wherein each downlink-operator base station and uplink-operator user determines only the relevant set of transmit precoders by exchanging minimal information among the coordinating base stations and user equipments. To minimize the coordination between the uplink-opeator users, we propose interference budget allocation scheme based on reference signal measurements from downlink-operator users. Numerical simulations are provided to compare the performance of proposed algorithms with and without the inter-micro-operator interference constraints

Energy efficiency optimization in millimeter wave backhaul heterogeneous networks

Within the last few years, there has been a massive growth in the number of wireless devices and internet connections. This is expected to continue during the next few years. To satisfy the resulting high data traffic demands, dramatic expansion of network infrastructures as well as fast escalation of energy demands are expected. Meanwhile, there has been a growing concern about the energy consumption of wireless communication systems and their global carbon footprint. To that end, future wireless systems must satisfy three main requirements. Firstly, they must provide users with very high throughput. Secondly, they must be able to provide seamless connectivity as well as ubiquitous access to the expected enormous number of users. Finally, they must achieve the first two points with less energy consumption. The requirements can be summarized into the joint optimization of energy efficiency (EE), user association and backhaul (BH) flow assignment, which remains a fundamental objective in the design of next generation networks. This thesis consists of two studies on EE maximization in heterogeneous networks (HetNets). In the first study, it is assumed that each user has already been associated to a single base station (BS). Under this setting, We consider enforcing a strict throughput demand on all user equipment (UEs), called joint EE, power, and flow control (JEEPF), versus allowing an acceptable range of demands for each, called joint EE, power, flow control, and throughput (JEEPFT). This minor change causes a drastic difference in the formulation of both problems. JEEPF is convex while JEEPFT is quasiconvex, for which we propose a bisection method-based approach. In the second study, the problem of user association is added to the joint optimization of EE, power and BH flow control, and an energy efficient user association, power and flow control (EEUAPF) algorithm is proposed. The original EEUAPF optimization problem is a non-convex mixed integer programming problem, and therefore NP-hard. We show how this non-convex problem can be tailored into a form that can be approached using a classical mathematical programming technique called column generation and convex programming to derive the optimal solution with a low complexity. Simulation results are used to demonstrate the EE gains of the proposed approaches in both studies

Enhanced Interference Management Techniques for Heterogeneous Cellular Networks

Interference management is one of the most challenging problems facing wireless communication networks, especially for the cellular wireless communication system that is based on reuse-one deployment. This problem becomes even more noteworthy in the heterogeneous cellular networks (HetNets) where lower power nodes (LPNs) are deployed in the coverage area of the macro base station (MBS). The higher transmit power possessed by the MBS, together with the cell selection procedure employed in HetNet: where a user equipment (UE) may be served by a closer LPN (to enable cell splitting) even though the received power from the MBS could be higher, are some factors that cause interference in HetNet. In the 5th generation mobile networks (5G) when the number of deployed LPNs increases interference will be more serious. This thesis proposes interference management techniques based on beamforming with different level of cooperation amongst base stations in HetNet. In this thesis, we first designed global beamforming vectors that will maximize the weighted sum-rate of HetNet while fulfilling some power and interference constraints. The interference constraint controls the allowable interference from the MBS to other UEs in the HetNet. The global beamforming vectors were achieved using the Branch and Bound technique which is a global optimization method used in solving non-convex optimization problems. The beamformers that maximize the weighted sum-rate of HetNet are designed jointly by all BSs in the HetNet, hence the implementation is done centrally. Since each UE in HetNet has peculiar interference situation, we design a UE-centric clustering scheme, which is capable of determining the BSs in the HetNet that interferes each UE the most at a particular time. Afterward, these BSs coordinate interference with the serving BS of this UE and make resource allocation decisions together to allocate beamforming directions and powers to each UE in the HetNet. This will spatially separate signals sent to each UE, thereby mitigating interference and improving the total data rate achievable in HetNet. HetNet tends to be distributed, also X2-interface which is the backhaul link that connects BSs in the HetNet has a limited capacity which makes it incapable of withstanding huge burdens in its backhaul. We, therefore, design distributed beamforming directions using only local channel state information available at each transmitter. We also develop optimal power allocation scheme for each UE in each cell to maximize the sum-rate of each cell in the HetNet

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic

Holistic resource management in UAV-assisted wireless networks

Unmanned aerial vehicles (UAVs) are considered as a promising solution to assist terrestrial networks in future wireless networks (i.e., beyond fifth-generation (B5G) and sixth-generation (6G)). The convergence of various technologies requires future wireless networks to provide multiple functionalities, including communication, computing, control, and caching (4C), necessary for applications such as connected robotics and autonomous systems. The majority of existing works consider the developments in 4C individually, which limits the cooperation among 4C for potential gains. UAVs have been recently introduced to supplement mobile edge computing (MEC) in terrestrial networks to reduce network latency by providing mobile resources at the network edge in future wireless networks. However, compared to ground base stations (BSs), the limited resources at the network edge call for holistic management of the resources, which requires joint optimization. We provide a comprehensive review of holistic resource management in UAV-assisted wireless networks. Integrated resource management considers the challenges associated with aerial networks (such as three-dimensional (3D) placement of UAVs, trajectory planning, channel modelling, and backhaul connectivity) and terrestrial networks (such as limited bandwidth, power, and interference). We present architectures (source-UAV-destination and UAV-destination architecture) and 4C in UAV-assisted wireless networks. We then provide a detailed discussion on resource management by categorizing the optimization problems into individual or combinations of two (communication and computation) or three (communication, computation and control). Moreover, solution approaches and performance metrics are discussed and analyzed for different objectives and problem types. We formulate a mathematical framework for holistic resource management to minimize the linear combination of network latency and cost for user association while guaranteeing the offloading, computing, and caching constraints. Binary decision variables are used to allocate offloading and computing resources. Since the decision variables are binary and constraints are linear, the formulated problem is a binary linear programming problem. We propose a heuristic algorithm based on the interior point method by exploiting the optimization structure of the problem to get a sub-optimal solution with less complexity. Simulation results show the effectiveness of the proposed work when compared to the optimal results obtained using branch and bound. Finally, we discuss insight into the potential future research areas to address the challenges of holistic resource management in UAV-assisted wireless networks