Showing papers on "Backhaul (telecommunications) published in 2020"

Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching

Abstract: Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.

White Paper on Broadband Connectivity in 6G

Abstract: This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goals, not only the terrestrial networks will be evolved but they will also be integrated with satellite networks, all facilitating autonomous systems and various interconnected structures. We believe that several categories of enablers at the infrastructure, spectrum, and protocol/ algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric and scalable cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. The latter path will lead to THz communications complemented by visible light communications in specific scenarios. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latencies, higher reliability, and reduced complexity. Different options will be needed to optimally support different use cases. The resource efficiency can be further improved by using various combinations of full-duplex radios, interference management based on rate-splitting, machine-learning-based optimization, coded caching, and broadcasting.

Integrated Access and Backhaul in 5G mmWave Networks: Potential and Challenges

Abstract: IAB is being considered as a means to reduce the deployment costs of ultra-dense 5G mmWave networks, using wireless backhaul links to relay the access traffic. In this work we describe the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base station, it is still necessary to validate the IAB performance in realistic scenarios. In this article we demonstrate the cell edge throughput advantage offered by IAB using endto- end system-level simulations. We also highlight some research challenges for IAB that will require further investigations.

Maritime Coverage Enhancement Using UAVs Coordinated With Hybrid Satellite-Terrestrial Networks

Abstract: Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

Scoring the Terabit/s Goal:Broadband Connectivity in 6G

Abstract: This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity.

Joint Cache Placement, Flight Trajectory, and Transmission Power Optimization for Multi-UAV Assisted Wireless Networks

Abstract: It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks

Abstract: We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where a DBS utilizes those sub-channels for access links of associated user equipment (UE) as well as for backhaul links of associated IAB nodes, and an IAB node can utilize all for its associated UEs. This is one of key features in which 5G differs from traditional settings where the backhaul networks are designed independently from the access networks. With the goal of maximizing the sum log-rate of all UE groups, we formulate the spectrum allocation problem into a mix-integer and non-linear programming. However, it is intractable to find an optimal solution especially when the IAB network is large and time-varying. To tackle this problem, we propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations and show promising results compared with some baseline allocation policies.

Ultra-Dense LEO Satellite Offloading for Terrestrial Networks: How Much to Pay the Satellite Operator?

Abstract: Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO’s original users Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Abstract: Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) with a potential to create opportunities for enhancing the capacity of the network by dynamically moving the supply towards the demand while facilitating the services that cannot be provided via other means efficiently. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAV-BS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAV-BS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAV-BS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization

Abstract: The use of unmanned aerial vehicle (UAV)- based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust the UAV locations are essential to maximize performance. In addition to careful tracking of spatio-temporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve moving users may improve the overall system performance even in the presence of backhaul capacity limitations.

On Optimal Resource Allocation for Hybrid VLC/RF Networks With Common Backhaul

Abstract: The synergy between visible light communication (VLC) and radio frequency (RF) networks has attracted a considerable amount of attention due to the envisioned improvements compared to conventional systems, mainly in terms of data rate and coverage. In this paper, we investigate for the first time the coexistence of VLC and RF networks, assuming that both networks are served by a common backhaul network, as well as both perfect and imperfect channel state information (CSI). In this context, we propose an optimal resource allocation scheme that maximizes the corresponding data rate, while also taking into account the fairness among the involved users. This is of paramount importance because in such heterogeneous networks, a standard rate maximization approach yields a severely degraded performance for the weaker users. In order to provide a tractable solution to the formulated problem, which is non-convex, we transform this into an equivalent convex one. Moreover, a simplified power allocation problem is solved, which provides comparable results with substantially lower complexity. Finally, extensive simulations illustrate the validity and effectiveness of the proposed analysis, and provide valuable insights on the impact of the imperfect CSI on the overall network performance.

Joint Computation Offloading and URLLC Resource Allocation for Collaborative MEC Assisted Cellular-V2X Networks

Abstract: By leveraging the 5G enabled V2X networks, the vehicles connected by cellular base-stations can support a wide variety of computation-intensive services. In order to solve the arisen challenges in end-to-end low-latency transmission and backhaul resources, mobile edge computing (MEC) is now regarded as a promising paradigm for 5G-V2X communications. Considering the importance of both reliability and delay in vehicle communication, this article innovatively envisions a joint computation and URLLC resource allocation strategy for collaborative MEC assisted cellular-V2X networks and formulate a jointly power consumption optimization problem while guaranteeing the network stability. To solve this NP hard problem, we decouple it into two sub-problems: URLLC resource allocation for multi-cells to multi-vehicles and computation resource decisions among local vehicle, serving MEC server and collaborative MEC server. Secondly, non-cooperative game and bipartite graph are introduced to reduce the inter-cell interference and decide the channel allocation, which aims to maximize the throughput with a guarantee of reliability in URLLC V2X communication. Then, an online Lyapunov optimization method is proposed to solve computation resource allocation to get a trade-off between the average weighted power consumption and delay where CPU frequency are calculated using Gauss-Seidel method. Finally, the simulation results demonstrate that our proposed strategy can get better trade-off performance among power consumption, overflow probability and execution delay than the one based on centralized MEC assisted V2X.

Offline SLA-Constrained Deep Learning for 5G Networks Reliable and Dynamic End-to-End Slicing

Abstract: In this paper, we address the issue of resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV)-based fifth generation (5G) networks. The different slices’ tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices’ traffics predictor based on a soft gated recurrent unit (GRU). We then build—at each virtual network function—joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely, violation rate -based SLA and resource bounds -based SLA. This is achieved by integrating dataset-dependent generalized non-convex constraints into the DNN offline optimization tasks that are solved via a non-zero sum two-player game strategy. In this respect, we highlight the role of the underlying hyperparameters in the trade-off between overprovisioning and slices’ isolation. Finally, using reliability theory, we provide a closed-form analysis for the lower bound of the so-called reliable convergence probability and showcase the effect of the violation rate on it.

3-D Trajectory Optimization in UAV-Assisted Cellular Networks Considering Antenna Radiation Pattern and Backhaul Constraint

Abstract: This article explores the effects of 3-D antenna radiation pattern and backhaul constraint on optimal 3-D path planning problem of an unmanned aerial vehicle (UAV), in interference prevalent downlink cellular networks. We consider a cellular-connected UAV that is tasked to travel between two locations within a fixed time, and it can be used to improve the cellular connectivity of ground users by acting as a relay. Since the antenna gain of a cellular base station changes significantly with the UAV altitude, the UAV can improve the signal quality in its backhaul link by changing its height over the course of its mission. This problem is nonconvex, and thus, we explore the dynamic programming technique to solve it. We show that the 3-D optimal paths can introduce significant network performance gain over the trajectories with fixed UAV heights.

QoE-Driven Transmission-Aware Cache Placement and Cooperative Beamforming Design in Cloud-RANs

Abstract: Pre-caching popular videos at base stations (BSs) is a cost-effective way to significantly alleviate the backhaul pressure. With the video caching, the cache placement and the transmission strategy are intertwined with each other and jointly affect the system performance. Furthermore, the cache placement is updated in a much longer timescale than the transmission strategy. In this paper, the long-term transmission-aware cache placement and the short-term transmission strategy are designed to enhance the quality of experience (QoE) for the video streaming in cloud radio access networks (cloud-RANs). Specifically, consider a cache-enabled cloud-RAN, video contents are cached at BSs, and user requests are cooperatively satisfied by multiple BSs via the cooperative beamforming. To improve the weighted sum of users’ QoE, the long-term transmission-aware caching problem in the caching stage and the short-term transmission problem in the delivery stage are respectively formulated, taking into account the backhaul capacity constraint, the transmission power constraint, and the storage size constraint. For the caching problem, the sample average approach is first used to approximate the long-term average QoE value. Then, cache placement strategies are devised in both centralized and distributed manner. For the transmission problem, the full-cooperative beamforming scheme is studied with the optimized cache placement, and an iterative algorithm is proposed. Simulation results show that our proposed transmission-aware cache placement and transmission strategies can achieve higher QoE performance than other cache placement and transmission strategies.

Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-Latency

Abstract: In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and the edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs’ transmit powers, the SBSs’ receive beamformers, and the SBSs’ transmit covariance matrices, which is a mixed-integer and non-convex optimization problem. Based on the methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.

Delay-Minimized Edge Caching in Heterogeneous Vehicular Networks: A Matching-Based Approach

Abstract: To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity

Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach

Abstract: In this paper, the problem of user association and resource allocation is studied for an integrated satellite-drone network (ISDN). In the considered model, drone base stations (DBSs) provide downlink connectivity to ground users whose demand cannot be satisfied by terrestrial small cell base stations (SBSs). Meanwhile, a satellite system and a set of terrestrial macrocell base stations (MBSs) are used to provide resources for backhaul connectivity for both DBSs and SBSs. For this scenario, one must jointly consider resource management over satellite-DBS/SBS backhaul links, MBS-DBS/SBS terrestrial backhaul links, and DBS/SBS-user radio access links as well as user association with DBSs and SBSs. This joint user association and resource allocation problem is modeled using a competitive market setting in which the transmission data is considered as a good that is being exchanged between users, DBSs, and SBSs that act as “buyers”, and DBSs, SBSs, MBSs, and the satellite that act as “sellers”. In this market, the quality-of-service (QoS) is used to capture the quality of the data transmission (defined as good), while the energy consumption the buyers use for data transmission is the cost of exchanging a good. According to the quality of goods, sellers in the market propose quotations to the buyers to sell their goods, while the buyers purchase the goods based on the quotation. The buyers profit from the difference between the earned QoS and the charged price, while the sellers profit from the difference between earned price and the energy spent for data transmission. The buyers and sellers in the market seek to reach a Walrasian equilibrium, at which all the goods are sold, and each of the devices’ profit is maximized. A heavy ball based iterative algorithm is proposed to compute the Walrasian equilibrium of the formulated market. Analytical results show that, with well-defined update step sizes, the proposed algorithm is guaranteed to reach one Walrasian equilibrium. Simulation results show that, at the achieved Walrasian equilibrium solution, the proposed algorithm can yield a two-fold gain in terms of the number of radio access links with a data rate of over 40 Mbps, and a three-fold gain in terms of the number of backhaul links with a data rate greater than 1.6 Gbps.

Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing

Abstract: The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

5G Wireless Transport in a PON Context: An Overview

Abstract: The high capacity of future 5G wireless networks must be supported by equivalent high-capacity fixed networks for backhaul and fronthaul (x-haul) transport. Especially the trend toward densification of radio networks, in which many small cells are located next to macrocells, will call for cost-efficient x-haul solutions to meet the overall business case targets. Optical access technologies bear great potential to offer low-cost solutions for these x-haul transport links. Starting from requirements on bandwidth, latency, and other critical parameters of 5G x-haul transport networks, ITU-T Q2/SG15 has elaborated a generic approach on how to employ PON technologies in various functional split architectures of wireless networks. The proposed PON architectures are presented and discussed in the recently released supplementary document G.Sup66, "5G Wireless Fronthaul Requirements in a PON Context," in October 2018. This article provides a high-level summary of the findings in Supplement G.Sup66.

A Service-Based Architecture for Enabling UAV Enhanced Network Services

Abstract: This article provides an overview of enhanced network services, while emphasizing the role of UAVs as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the SBA to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an ACS function responsible for the control and orchestration of a UAV fleet.

Cache-Enabling UAV Communications: Network Deployment and Resource Allocation

Abstract: In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization problem, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets

Abstract: Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

Technology Roadmap for Beyond 5G Wireless Connectivity in D-band

Abstract: Wireless communication in millimeter wave bands, namely above 20 GHz and up to 300 GHz is foreseen as a key enabler technology for the next generation of wireless systems. The huge available bandwidth is contemplated to achieve high data rate wireless communications, and hence, to fulfill the requirements of future wireless networks. Several Beyond 5G applications are considered for these systems: high capacity backhaul, enhanced hot-spot kiosk as well as short-range Device-to-Device communications. In this paper we propose to discuss the trade-offs between scenario requirements and current silicon technologies limits to draw a technology roadmap for the next generation of wireless connectivity in D-band.

Resource Allocation and Node Placement in Multi-Hop Heterogeneous Integrated-Access-and-Backhaul Networks

Abstract: We consider a heterogeneous MIMO-OFDMA based dense small cell (SC) system in which each macro cell base station (MBS) serves its coverage area with the help of small cell base stations (SBSs) through multi-hop wireless connections. The SBSs act as integrated access and backhaul (IAB) nodes that handle both access and backhaul traffics with wireless links. We first develop an optimal (sum-rate maximization) resource allocation (RA) algorithm which considers subcarriers/spatial subchannels assignment and the associated power allocations. We also present two low-complexity suboptimal RA schemes which, as verified by simulations, incur only minor performance loss in the high SNR region. Our RA algorithms can be applied to other multi-hop networks with general UE association rule and node location distributions. We study the channel aging effect caused by the time lag between the time channel state information (CSI) is measured and that when data transmission occurs. We show the benefit of channel prediction and the limit of a centralized RA approach. The advantages of frequency (channel) reuse and the multi-hop architecture are demonstrated as well. A related but perhaps more important system design issue for an IAB cellular network is the IAB node placement problem. With the given UE association rule and UE location distribution, we present systematic approaches to find the optimal node locations. For two special propagation models, we derive closed-form expressions for the node locations that maximizes a spectral efficiency lower bound. Numerical results validate the accuracy of our estimates based on either numerical evaluations or closed-form solutions.

3D Drone Base Station Placement and Resource Allocation With FSO-Based Backhaul in Hotspots

Abstract: Deploying a Drone Base Station (DBS) over a hotspot area is a promising solution to improve the user Quality of Service (QoS) by helping the Macro Base Station (MBS) transmit traffic to the users. Essentially, the DBS, which works as a relay node between the users and the MBS, can increase the users’ data rates by virtue of more likely short-distance Line of Sight (LoS) communication links. Furthermore, deploying DBS is more cost-effective and flexible as compared to deploying small cells. The DBS can employ Free Space Optical (FSO) links for backhauling between the DBS and the MBS. In this paper, we study the 3D deployment and resource allocation of a DBS in a given hotspot area with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power. To solve the problem, we first decompose the primal problem into two subproblems, i.e., the 3D DBS placement problem and the resource allocation problem. Second, we propose a cyclic iterative algorithm to solve the two sub-problems separately and use the output of one as the input of the other. The performance of the algorithm is demonstrated via extensive simulations.

Key Enabling Technologies for the Post-5G Era: Fully Adaptive, All-Spectra Coordinated Radio Access Network with Function Decoupling

Abstract: With the rapidly growing bandwidth demand for wireless applications, new system technologies related to post-5G are emerging. In this article, an all-spectra fully adaptive and coordinated radio access network (RAN) is reported and discussed. By employing a fiber-wireless integration and networking architecture, all data-carrying channels could be aggregated in the same fiber access infrastructure. This enables a coordinated RAN with function decoupling, in which lower RF, 5G New Radio (NR), sub-THz, and even lightwave are employed; also, different types of services are delivered depending on their physical layer properties. Promising scenarios are discussed, such as integrated access of wireless NR-free space optical (FSO) backhauling and indoor systems via visible light communication (VLC) and efficient NR beamforming aided by VLC positioning system. The former use case can enhance the network throughput and reliability. This is because both FSO and NR can support high channel capacity due to their abundant bandwidth. Meanwhile, with the advancement of novel DSP techniques, the stability of the NR-FSO link under diverse weather turbulences or suffering from burst mode interference can be enhanced. The latter scenario provides an alternative solution for high-speed data link and a simplified beam management via the VLC-aided positioning system. VLC can concurrently provide ubiquitous indoor illumination, data transmission, and positioning. With the help of artificial intelligence algorithms, a VLC-based precision positioning system can provide a location accuracy of less than 1 cm, and it is able to meet the narrow beam size of the NR beamformer in a 3D model. Therefore, it is foreseeable that an all-spectra function decoupled RAN can serve as a unified network platform to support all wireless applications while optimizing system throughput, channel condition, network coverage, and software/ hardware complexity for post-5G mobile data networks.

IoT Based Smart Farming: Are the LPWAN Technologies Suitable for Remote Communication?

Abstract: The Internet of Things (IoT) has changed the definition of smart farming and enhanced it’s capabilities to monitor and assess crop and soil quality; to plan planting locations to optimize resources and land area. The Low-Power Wide-Area Network (LPWAN) technologies have enhanced these capabilities by increasing the wireless communication range, by eliminating the dependency of Backhaul networks and by reducing power consumption. In this paper, we have presented an experimental analysis of LPWAN literature with the support of simulation and actual implementation of a Long Range Wide Area Network (LoRaWAN) based IoT network for smart farming. Based on our evaluation and experiment of the existing work and the practical implementation of IoT based smart sprinkler using LoRaWAN communication protocol, this paper has presented a comparison and evaluation of different LPWAN technologies for remote smart farming. The empirical equation of wireless communication range of LoRaWAN gateways and power consumption model of LoRaWAN end devices helped us to determine that, the LoRaWAN communication system enables an IoT network to be deployed over 10 kilometers wirelessly in remote settings without being dependent on a Long Term Evolution (LTE-4G/5G) or other backhaul network and the end devices consume as low energy as only 15.36mAh per day.

Joint Incentive and Resource Allocation Design for User Provided Network Under 5G Integrated Access and Backhaul Networks

Abstract: User provided network (UPN) allows a user with high channel quality to share the network access for users with poor channel quality. As a result, both the quality of experience and efficiency of network resource can be improved by UPN. However, the success of UPN relies heavily on the willingness of users to participate in sharing, so the design of incentive mechanisms is critical for UPN. In this paper, the UPN formed by D2D links under 5G integrated access and backhaul (IAB) networks is considered. In IAB networks, both access and backhaul links use wireless transmissions and dynamically share all the spectrum resources. Thus, the resource allocation for all the links also has a great impact on the UPN efficiency. To this end, a novel joint incentive and resource allocation design is explored. More specifically, considering the fairness between users, a Nash bargaining problem as a cooperative game is formulated by considering the user utility, the sensitivity of battery energy, the incentive compensation, and the limitation of network resources. To achieve the optimal Nash bargaining solution, a centralized algorithm is first designed, in which all the user information is collected by the operator for conducting centralized optimization. Thus, the centralized algorithm leads to a privacy problem. To this end, a distributed algorithm is developed to decompose the primal problem into subproblems for the operator and each user. By passing intermediate parameters between users and iterative execution of subproblems, the solution of the distributed algorithm is proved to converge to the optimal solution of the centralized algorithm. Extensive numerical results have been conducted to show that our design can effectively improve both the user experience and network throughput, i.e., operator's revenue.