Wireless backhaul networks: centralised vs. distributed scenario

In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macro cell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs' positions.

In this paper, we consider the optimization of wireless capacity-limited back-haul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the back-haul links, we propose to use a capacity-aware beam-forming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the back-haul up-link capacity and the access up-links signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware back-haul link scheme achieves similar or better performance than traditional wireless back-haul links and requires considerably less computational complexity.

Wireless backhaul and inter-tower communications networks were previously proposed as key enabling technologies for the next generation digital broadcasting systems, e.g. the Advanced Television Systems Committee (ATSC) 3.0 system. Due to the backward compatibility constraint, conventional symmetrical multi-input multi-output (MIMO) techniques cannot be applied directly in the existing broadcast infrastructure. In this paper, we propose a non-symmetrical approach to integrate MIMO in wireless backhaul and inter-tower communications networks. The proposed schemes maintain the existing broadcast infrastructure, while only adding a low-power RF feeding cable and one or more highly directional antennas to achieve low-cost MIMO implementation for high throughput data distribution and inter-tower networking for the next generation broadcasting systems. The advantages of the proposed system are low deployment cost and full backward compatibility with the ATSC 3.0 broadcast services.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocell base station (MBS) uses conventional power supply. Given a stochastic energy arrival process for the SBSs, we derive a power control policy for the downlink transmission of both MBS and SBSs such that they can achieve their objectives (e.g., maintain the signal-to-interference-plus-noise ratio (SINR) at an acceptable level) on a given transmission channel. We consider a centralized energy harvesting mechanism for SBSs, i.e., there is a central energy storage (CES) where energy is harvested and then distributed to the SBSs. When the number of SBSs is small, the game between the CES and the MBS is modeled as a single-controller stochastic game and the equilibrium policies are obtained as a solution of a quadratic programming problem. However, when the number of SBSs tends to infinity (i.e., a highly dense network), the centralized scheme becomes infeasible, and therefore, we use a mean field stochastic game to obtain a distributed power control policy for each SBS. By solving a system of partial differential equations, we derive the power control policy of SBSs given the knowledge of mean field distribution and the available harvested energy levels in the batteries of the SBSs.

The joint application of wireless backhaul networks and non-orthogonal multiple access (NOMA) hold the potential to fulfill the increasing demands of fifth-generation (5G) communication networks and beyond. It is usual in wireless backhaul networks to take assistance from small cell base stations acting as intermediate relays to reach the remote destination. This cooperative communication is an acknowledged technique to combat multi-path fading, improve energy efficiency, and enhance the reliability and capacity of wireless networks. This article studies the application of relay selection (RS) in the proposed NOMA-based cooperative wireless backhaul network model. The system performance is analyzed through closed-form and asymptotic expressions of the sum ergodic rate. The paper also presents practical design guidelines of optimum location for small base stations of wireless backhaul networks.

In order to improve the total system throughput by analyzing the resource allocation problems in backhaul links and access links of base stations in ultra-dense network (UDN), a proportional fair resource allocation (PFRA) algorithm was designed for the wireless hybrid hierarchical backhaul network in this paper. First, the base stations were stratified and the frame structure of each base station in different layeies was re-designed. Then, the objective function was constructed based on the proportional fair utility function to optimize the system throughput. Finally, an iterative algorithm was derived based on Lagrange Multiplier algorithm to obtain the optimal solution of spectrum resource allocation in hybrid hierarchical backhaul network. The performance of the PFRA algorithm has been simulated and the results show that the PFRA algorithm can improve network throughput effectively at the cost of losing a certain network coverage rate. In the future, PFRA needs to be optimized to reach the best balance between network throughput and network coverage rate.

mmWave communication has been recognized as a highly promising technology for 5G wireless backhaul, which is capable of providing multi-gigabit per second transmission rates. However, in urban wireless backhaul environments, unforeseen events can cause short-term blockages or node failures and, therefore, network survivability is extremely important. In this paper, we investigate a novel relay-assisted mmWave backhaul network architecture, where a number of small-cell BSs and relays are deployed, e.g. on the lampposts of urban streets. Relays are used to provide multi-hop line-of-sight paths between small-cell BSs, which form logical links of the network. In this scenario, the interconnected logical links make up a mesh network, which offers opportunities for both link-level and network-level reconfiguration. We propose two joint link-network level reconfiguration schemes for recovery after exceptional events. One prioritizes relay path (link-level) reconfiguration and uses alternate network-level paths only if necessary. The other splits traffic on both reconfigured links and backup paths to improve network throughput. Simulation results demonstrate that the proposed schemes significantly outperform purely link-level and purely network-level reconfiguration schemes. The proposed approaches are shown to not only maintain high network throughput but to also provide robust blockage/fault tolerance across a range of scenarios for urban mmWave backhaul networks.

To investigate how massive multiple-input multipleoutput (MIMO) impacts the data transmission of a dense heterogeneous network (HetNet), we study its uplink transmission that consists of two users communicating with a macro-cell base station (MCBS) through a small-cell BS (SCBS) with zero-forcing (ZF) detection at each BS. We first analyze the scheme with quantize-forward (QF) relaying at the SCBS and joint decoding (JD) at the MCBS for both users' messages. To maximize the rate region, we derive the optimal quantization at the SCBS for both users' data streams and show how they depend on the large-scale fading. We further propose a new scheme that simplifies the QF-JD scheme through Wyner-Ziv (WZ) binning and time division (TD) transmission at the SCBS to allow not only sequential but also separate decoding of each user's message at the MCBS. For this QF-WZTD scheme, the optimal quantization parameters are identical to that of the QF-JD scheme while the phase durations and power allocation are conveniently optimized as functions of the quantization parameters. Despite its simplicity, the QF-WZTD scheme achieves the same rate region of the QFJD scheme, making it an attractive option for the fifth-generation HetNets.

The general trend in the backhaul link technology arena is the increasing use of high-speed microwave solutions. The ever-rising demand for high capacities at low cost has recently enhanced the interest in wireless backhaul networks. Evaluating the connectivity of the wireless multi-hop backhaul networks is a significant task. Taking into account the high frequency of operation, a novel analytical and engineering propagation model is presented for the calculation of connectivity. Furthermore, the minimum required node density in order to keep the network connected is also calculated. The sensitivity of network connectivity on frequency operation, transmission power, and climatic conditions is also investigated. Finally, the error performance of the backhaul network links is finally presented, and some very useful conclusions are drawn.

Capacity has been an important issue for many wireless backhaul networks. Both the multihop nature and the large per packet channel access overhead can lead to its low channel efficiency. The problem may get even worse when there are many applications transmitting packets with small data payloads, e.g., Voice over Internet protocol (VoIP). Previously, the use of multiple parallel channels and employing packet concatenation were treated as separate solutions to these problems. However, there is no available work on the integrated design and performance analysis of a complete scheduler architecture combining these two schemes. In this paper, we propose a scheduler that concatenates small packets into large frames and sends them through multiple parallel channels with an intelligent channel selection algorithm between neighboring nodes. Besides the expected capacity improvements, we also derive delay bounds for this scheduler. Based on the delay bound formula, call admission control (CAC) of a broad range of scheduling algorithms can be obtained. We demonstrate the significant capacity and resequencing delay improvements of this novel design with a voice-data traffic mixing example, via both numerical and simulation results. It is shown that the proposed packet concatenation and channel selection algorithms greatly outperform the round-robin scheduler in a multihop scenario.

The enormous increase in powerful mobile devices has created hype for mobile data traffic. The demand for high definition images and good quality video streaming for the mobile users has constantly being escalated over the recent decade. In particular, the newly emerging mobile Augmented Reality and Virtual Reality (AR/VR) applications are anticipated to be among the most demanding applications over wireless networks so far. The architecture of the cellular networks has been centralized over the years, which makes the wireless link capacity, bandwidth and backhaul network difficult to cope with the explosive growth in the mobile user traffic. Along with the rise in overall network traffic, mobile users tend to seek similar types of data at different time instants creating a bottleneck in the backhaul link. To overcome such challenges in a network, emerging techniques of caching the popular content and performing computation at the edge are gaining importance. The emergence of such techniques for near future 5G networks would pose less pressure on the backhaul links as well as the cloud servers, thereby, reducing the end-to-end latency of AR/VR applications. This paper surveys the recent edge computing techniques along with the powerful caching strategies at the edge and provides a roadmap for 5G and beyond wireless networks in the context of emerging applications.

The Integrated Access and Backhaul (IAB) technology provides a new view of the backhauling problem, especially when targeting end-to-end service provisioning. The IAB paradigm - being well aligned with the vision of Unmanned Aerial Vehicles (UAVs) - when combined with the benefits of the Reconfigurable Intelligent Surface (RIS) technology, offers to the future wireless networks the attributes of reconfigurability and energy efficiency. In this paper, we demonstrate the prospect of RIS in an UAV-assisted IAB network targeting energy-efficient operation, while accounting for the transmissions established at both the wireless access and backhaul network parts. We introduce a dynamic resource management framework that targets end-to-end energy efficiency optimization, while considering as controllable parameters the RIS elements’ phase shifts, the system bandwidth split between access and backhaul, and the transmission powers of both the UAV and the users. The corresponding joint optimization problem is formulated and treated via a distributed single-leader multiple-followers Stackelberg game-theoretic approach. The adaptation of the proposed framework is, also, demonstrated for the treatment of the IAB network’s end-to-end data rate optimization. The framework’s performance evaluation highlights the benefits achieved at both the users’ and UAV’s energy efficiency, by the joint exploitation of UAVs, IAB, and RIS technologies.

An Ultra-Dense Network (UDN) is foreseen as a key element of 5G, where small cells operating at millimeter-wave frequencies will be placed in hotspots to accommodate extreme traffic volumes. A fully distributed wired backhaul is not feasible in these UDN deployments because it is not practical to connect each small cell to the core over fiber. A likely substitute is wireless backhaul, where small cells are connected to one or few gateways over multi-hop millimeter-wave wireless links. The backhaul network capacity of the wireless backhaul is directly related to the average number of simultaneous transmissions in the wireless backhaul of an UDN. The problem of finding the average number of simultaneous transmissions is analogous to the minimum coloring problem, which is NP-Hard. In this paper, we adapt our previously proposed heuristic algorithm for solving this problem and then study the effect of varying the interference range of small cells on the backhaul network capacity. From the results of our Monte Carlo simulations, it is observed that the backhaul network capacity decreases significantly as the interference range increases.

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

In high-density indoor environments (e.g., airports or malls), it is expected an increase in data demand, in the number of applications having stringent Quality-of-Service requirements, and in the number of services among nearby users, such as video sharing or gaming. In order to satisfy these requirements, we adopt D2D communications, 60 GHz band transmissions, and adaptive beamforming techniques. The symbiosis of these technologies provides notable energy saving, high data rate, and strong interference reduction. Despite these benefits, a major issue is the limited communication range, due to the high path loss. To overcome this issue, we consider a Millimeter-Wave Mobile Broadband (MMB) system, consisting of several Access Points interconnected among themselves through a wireless backhaul network. Based on the specific features of the technologies adopted for the considered MMB system, we aim to improve the transmission efficiency, by exploiting concurrent transmissions. Therefore, we propose a new centralized access control scheme which jointly manages D2D communications and transmissions in the access and the backhaul network. Our control strategy aims to maximize the system throughput, to minimize the end-to-end delay, and to improve the fairness among users, while taking into account to keep the computational load as low as possible. To meet these goals, our proposed access control scheme is composed of a data flow management strategy and a multi-criteria scheduling algorithm based on greedy graph vertex-coloring techniques.