Integrated Access And Backhaul Research Articles

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning

Integrated access and backhaul (IAB) networks offer transformative benefits, primarily their deployment flexibility in locations where fixed backhaul faces logistical or financial challenges. This flexibility is further enhanced by IAB’s inherent ability for adaptive network expansion. However, existing IAB network planning models, which are grounded in the facility location problem and are predominantly addressed through linear programming, tend to neglect crucial geographical constraints. These constraints arise from the specific deployment constraints related to the positioning of IAB donors to the core network, and the geographic specificity required for IAB-node placements. These aspects expose an evident research void. To bridge this, our research introduces a geographically aware optimization methodology tailored for IAB deployments. In this paper, we detail strategies for both single-hop and multi-hop situations, concentrating on IAB donors distribution and geographical constraints. Uniquely in this study, we employ the inherent data rate limitations of network nodes to determine the maximum feasible hops, differing from traditional fixed maximum hop count methods. We devise two optimization schemes for single-hop and multi-hop settings and introduce a greedy algorithm to effectively address the non-convex multi-hop challenge. Extensive simulations across various conditions (such as diverse donor numbers and node separations) were undertaken, with the outcomes assessed against the benchmark of the single-hop scenario’s optimal solution. Our findings reveal that the introduced algorithm delivers efficient performance for geographically constrained network planning.

Adaptive Channel Detection for Full-Duplex IAB Systems on Unlicensed Bands

The integration of integrated access and backhaul (IAB) and full-duplex communications on unlicensed bands can improve the spectrum efficiency and mitigate the spectrum resource shortage. To guarantee fair coexistence on unlicensed spectrum, the listen-before-talk mechanism needs to be executed at the IAB nodes, where the clear channel assessment (CCA) is applied before transmission. However, the residual self-interference (RSI) brought by the full-duplex communication may degrade the CCA detection performance tremendously. Therefore, in this paper we propose an adaptive channel detection scheme for the full-duplex IAB network to minimize the impact of RSI on the CCA detection, which can enhance the spectrum efficiency. In particular, the closed-form expressions of false alarm probability and detection probability are derived. Accordingly, the energy detection (ED) threshold applied in CCA is adjusted adaptively to improve the system throughput. Then, a novel timing sequence with respect to the self-interference cancellation and CCA detection is proposed. The corresponding optimal initial threshold is also derived to realize the adaptive ED threshold. Furthermore, an enhanced request to send/clear to send mechanism is proposed to solve the hidden terminal problem. Simulation results demonstrate that the proposed schemes can effectively reduce the impact of RSI on the CCA detection accuracy and significantly improve the system throughput.

Joint association and resource allocation for multi-hop integrated access and backhaul (IAB) network

Joint association and resource allocation for multi-hop integrated access and backhaul (IAB) network

Packet-Level Throughput Analysis and Energy Efficiency Optimization for UAV-Assisted IAB Heterogeneous Cellular Networks

In this paper, we investigate the packet-level throughput and energy efficiency of millimeter-wave unmanned aerial vehicle (UAV)-assisted integrated access and backhaul (IAB) heterogeneous cellular networks with spatiotemporal traffic. Specifically, we develop a theoretical framework to analyze the mean packet throughput and energy efficiency of the network based on stochastic geometry and queueing theory, whereby the spatial randomness of network deployment and the temporal randomness of network traffic can be appropriately characterized. Different from the traditional network performance metrics emphasizing transmission and resource consumption, the packet-level performance helps to better understand the impact of not only packet transmission but also packet waiting time in a multihop network. Simulation results demonstrate that the assistance of IAB-based UAVs can efficiently relieve the load of terrestrial macro- and small-cell networks, and the appropriate network deployment parameters play pivotal roles in improving both the packet-level throughput and energy efficiency performance. By jointly optimizing the key network parameters, the packet energy efficiency can be significantly improved while ensuring the required mean packet throughput of the network.

RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points

Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a flying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconfigurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a fixed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs offers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.

MinFAST: Minimum Span Frequency Assignment Technique for Integrated Access and Backhaul Networks

We propose novel centralized minimum span frequency assignment (MS-FA) techniques allowing partially overlapped channels (POCs) to improve cost and spectral efficiency for integrated access and backhaul (IAB) networks. We first revisit the conventional MS-FA problem (MS-FAP) that minimizes the required bandwidth for given quality-of-service (QoS) requirements, and then design a novel integer linear programming (ILP)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</i> MS-FA technique with POCs. However, the optimal technique may not be feasible for large-scale IAB networks due to its NP-hardness. Hence, we propose a novel low-complexity MS-FA algorithm that achieves a near-optimal performance while significantly reducing the computational complexity. To the best of our knowledge, no MS-FA technique exists to consider both POCs and the physical interference model in the literature. Simulation results show that the spectral efficiency becomes significantly improved by reducing the required bandwidth in practical IAB networks.

A 39 GHz spatial division multiplex MU-MIMO using 256 element hybrid AAS for IAB application

AbstractThis paper describes the design and implementation of a 39 GHz 256-element hybrid active phased array antenna system with 16 individual digital transceivers, and its wireless multi-user, multi-input, multi-output verification. An earlier version of this paper was presented at the 2021 51st European Microwave Conference and was published in its Proceedings [1]. Using the prototype device, a single carrier transmission test assuming a mobile backhaul link and a MU-MIMO transmission test using a zero-forced orthogonal multi-beam based on channel reciprocity assuming an access link was performed. As a result, an EIRP of 43 dBm and a data rate of 5.5 Gbps in 1 GHz bandwidth single carrier 128QAM operation and an estimated total throughput of 2.4 Gbps by 3GPP TS38.214 [2] in 100 MHz bandwidth OFDM 8 MU-MIMO operation were achieved, respectively. As far as the author knows, this is the first demonstration for integrated access and backhaul (IAB) application that uses the 39 GHz band.

System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave (mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both full-duplex (FD)-IAB and half-duplex (HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB, FD-IAB can improve throughput by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and reduce latency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.

Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems

The roll-outs of fifth-generation (5G) New Radio (NR) systems operating in the millimeter-wave (mmWave) frequency band are essential for satisfying IMT-2020 requirements set forth by ITU-R in terms of the data rate at the access interface. To overcome mmWave-specific propagation phenomena, a number of radio access network densification options have been proposed, including a conventional base station (BS) as well as integrated access and backhaul (IAB) with terrestrial and aerial IAB nodes. The aim of this paper is to qualitatively and quantitatively compare the proposed deployments using coverage, spectral efficiency and BS density as the main metrics of interest. To this end, we develop a model capturing the specifics of various deployment options. Our numerical results demonstrate that, while the implementation of terrestrial relaying nodes potentially improves coverage and spectral efficiency, aerial relays provide the highest coverage, three times that of a direct link connection, and also significantly reduce the required BS density. The main benefit is provided by the link between the BS and the aerial relay. However, gains are highly dependent on a number of elements in antenna arrays and targeted outage probability. The use of terrestrial relays can be considered a natural trade-off between coverage and the aggregate rate.

A Backhaul Adaptation Scheme for IAB Networks Using Deep Reinforcement Learning With Recursive Discrete Choice Model

Challenges such as backhaul availability and backhaul scalability have continued to outweigh the progress of integrated access and backhaul (IAB) networks that enable multi-hop backhauling in 5G networks. These challenges, which are predominant in poor wireless channel conditions such as foliage, may lead to high energy consumption and packet losses. It is essential that the IAB topology enables efficient traffic flow by minimizing congestion and increasing robustness to backhaul failure. This article proposes a backhaul adaptation scheme that is controlled by the load on the access side of the network. The routing problem is formulated as a constrained Markov decision process and solved using a dual decomposition approach due to the existence of explicit and implicit constraints. A deep reinforcement learning (DRL) strategy that takes advantage of a recursive discrete choice model (RDCM) is then proposed. The incorporation of the RDCM helps to address the challenges of backhaul availability and scalability with the aim to increase robustness to backhaul failure in IAB networks. The advantage of applying the RDCM for this problem is that it incorporates choice aversion from prospect theory and the reward is not the only factor affecting the learning rates, but also the punishment. The performance of the proposed algorithm was compared to that of conventional DRL, i.e., without RDCM, and generative model-based learning (GMBL) algorithms. The simulation results of the proposed approach reveal risk perception by introducing certain biases on alternative choices. For instance, applying cumulative prospect theory in the context of route choices reveal different node behaviors as the learning rate and noise levels are varied. The simulation results showed that the proposed algorithm provides better throughput and delay performance over the two baselines.

Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells

With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the access and backhaul links. Considering the non-convex and combinatorial characteristics of the optimization problem and the dynamic nature of the mmWave link, we propose a multi-agent deep reinforcement learning (MADRL) based scheme to maximize the long-term total link throughput of the network. The simulation results show that the scheme can not only adjust user association and backhaul resource allocation strategy according to the dynamics in the access link state, but also effectively improve the link throughput under different system configurations.

Integrated Access and Backhaul (IAB) in Cell-Free Massive MIMO Systems

Integrated Access and Backhaul (IAB) in Cell-Free Massive MIMO Systems

On the Properties of Next Generation Wireless Backhaul

With the advent of 5G, cellular networks require a high number of base stations, possibly interconnected with wireless links, an evolution introduced in the last revision of 5G as the Integrated Access and Backhaul (IAB). Researchers are now working to optimize the complex topologies of the backhaul network, using synthetic models for the underlying visibility graph, i.e., the graph of possible connections between the base stations. The goal of this paper is to provide a novel methodology to generate visibility graphs starting from real data (and the data sets themselves together with the source code for their manipulation), in order to base the IAB design and optimization on assumptions that are as close as possible to reality. We introduce a GPU-based method to create visibility graphs from open data, we analyze the properties of the realistic visibility graphs, and we show that different geographic areas produce very different graphs. We run state-of-the-art algorithms to create wireless backhaul networks on top of visibility graphs, and we show that the results that exploit synthetic models are far from those that use our realistic graphs. Our conclusion is that the data-based approach we propose is essential to design mobile networks that work in a variety of real-world situations.

Coordinated parallel resource allocation for integrated access and backhaul networks

The architecture of integrated access and backhaul (IAB) endows 5G and beyond networks with the capabilities of both centralized and distributed resource management: a centralized node, IAB-donor, can efficiently coordinate resources with a network-wide view, and other distributed nodes, IAB-nodes, can quickly react to local traffic bursts multiple hops away from the IAB-donor. However, existing efforts on IAB networking mostly focus on either the centralized or distributed capability of IAB networks and lack the studies on handling bursty traffic. In this paper, a Coordinated Parallel Resource allocation scheme (CPReal) is developed to handle both bursty and non-bursty traffic. Its key feature is that it consists of both a centralized scheme at the IAB-donor for efficient coordination of resource allocation and a distributed scheme at each IAB-node for quick response to short-term bursty traffic. CPReal is standard-compliant and considers the strict parent-to-child relation between IAB-nodes specified in 5G standards. In CPReal, with the assistance from their parent nodes, IAB-nodes can quickly reach an agreement on resource allocation in a distributed and conflict-free way for traffic bursts. Extensive simulations show that CPReal outperforms existing schemes in throughput by 67.3% under bursty traffic and in end-to-end delay by up to 34.4% under various scenarios.

Assessing Latency of Packet Delivery in the 5G 3GPP Integrated Access and Backhaul Architecture with Half-Duplex Constraints

Integrated Access and Backhaul (IAB) is an enabling technology for efficient 5G millimeter wave (mmWave) New Radio (NR) deployment. The key feature of IAB is multi-hop wireless backhauling, allowing utilizing relaying IAB-nodes to provide cost-efficient access network densification and alleviate the problem of blockages. One of the critical performance measures in such systems is the latency of packet delivery over the multi-hop paths. The paper aims at assessing the impact of multi-hop transmission on the end-to-end delay in an IAB radio access network, taking into account the half-duplex constraint. We build a detailed queuing theory model for latency assessment in time-division-multiplexing (TDM)-based IAB deployments and evaluate the delay due to queuing in the network nodes for several cell topologies and under different time allocation strategies between access and backhaul. The paper considers a practical Manhattan-style urban deployment, which is characteristically impaired by the blockage of buildings. The numerical results show that balancing the access and backhaul micro phases is crucial for reducing the end-to-end packet delay, at least in the uplink, while increasing the number of network hops yields a linear increase in the total packet delay for both the uplink and downlink. The numerical results were obtained via simulation using the open-source software OMNeT++.

Routing and Resource Allocation for IAB Multi-Hop Network in 5G Advanced

Integrated access and backhaul (IAB) is a novel feature for extending the network coverage in 5G cellular networks, based on sharing/efficient allocation of owner’s spectrum traditionally reserved for access. However, since ultra-reliability and low latency (URLLC) requirements are a key component of 5G advanced services, provisioning such services present stringent challenges for IAB multi-hop network design. To fulfill the URLLC requirements in the IAB network, we propose a cross-layer design on routing and resource allocation under the current 3rd Generation Partnership Project (3GPP) 5G standards. We first formulate a routing problem for the IAB multi-hop network, which minimizes the latency while satisfying the reliability requirement. Subsequently, we present a reinforcement learning (RL) framework to solve the resource allocation and routing problem based on the local information of each agent (IAB node) in the environment. Afterward, we propose a novel entropy-based RL algorithm with federated learning (FL) mechanism to improve the overall performance as well as accelerate the convergence speed. Via the simulation, the proposed algorithm outperforms baseline algorithms from the latency and reliability perspective, respectively. Meanwhile, the convergence speed with the proposed algorithm also improves by using FL.

Two stage downlink scheduling for balancing QoS in multihop IAB networks

The 3GPP has envisioned Integrated Access and Backhaul (IAB) as a key enabler to support the flexible and ultra-dense deployment of 5G cells with significantly reduced deployment costs. However, IAB introduces new research challenges, especially when studying multihop topology. This paper presents a QoS-based downlink scheduler designed explicitly for IAB networks. The scheduler is devised after considering multihop relaying topology, QoS requirements, and backhaul constraints. We investigate its performance using system-level simulations under diverse IAB topologies. The performance results show that the scheduler is capable of fulfilling QoS requirements for different types of services, even at heavy network load. The scheduler also maintains excellent fairness among QoS flows belonging to the same service type.

Self-backhauling failure mitigation using 5G new radio

5G network operators will consider the dense deployment of small cells to increase network coverage and capacity. However, in order to install a large number of gNBs, operators will face the challenge of backhauling their traffic to the core network in a cost-effective manner. Although Integrated Access and Backhaul (IAB) using 5G new radio is a promising solution to solve the dilemma of small cells’ backhauling, densifying the network will increase the probability of failure of these links. To cope with this problem, a self-healing scheme is used to mitigate or at least alleviate the effect of backhaul failure. We propose to use IAB with the collaboration of neighboring gNBs to mitigate the failed backhaul link(s). Hence, our goal is to design a pre-planned network capable of providing the minimum rate requirements to its users in the presence of backhaul failure. We formulate a joint resource allocation/backhaul outage compensation optimization problem as a non-convex mixed integer non-linear program. Due to the difficulty of this problem, we divide this problem into two sub-problems, in addition to the use of different approximation and relaxation techniques to find an approximated sub-optimal solution. Simulation results show that the proposed scheme is capable of providing the network users an acceptable continuous, albeit slightly degraded, service during backhaul failure. The degradation percentage is inversely proportional to the network densification for single and multiple failures. Meanwhile, the Degree of Recovery (DoR), i.e., subtracting the degradation percentage from 100, of single failure scenarios range from 95% (for the best case) to 54% (for the worst case). For the multiple failures scenario, the DoR is much lower and can reach zero in case of three concurrent failures

Integrated Access and Backhaul in Millimeter-Wave Cellular: Benefits and Challenges

The recently proposed NR-ready integrated access and backhaul (IAB) architecture promises to bring a cost-efficient deployment solution for both coverage extension and capacity boosting in the emerging 5G/5G+ systems. While its impact on the coverage extension was thoroughly addressed in the literature, the effect of advanced functionalities such as multihop, multi-connectivity, and multi-beam operations on the throughput remained unclear. We review and characterize the system-level impact of these capabilities on the performance of self-backhauled IAB systems operating in half-duplex mode and utilizing millimeter-wave (mmWave) technology across both access and backhaul. Our results indicate that the throughput gain of multihopping and multi-beaming is significant even without multi-connectivity operation. Another important lesson is that in all-mmWave systems with link blockage, multi-connectivity with link switching allows achieving self-load balancing. Finally, we outline future research directions.