Integrated Access And Backhaul Networks 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.

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.

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.

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.

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.

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.

Resource Allocation and System Design of Out-Band Based Integrated Access and Backhaul Network at mmWave Band

To enhance the coverage and network capacity of the mmWave based 5 G new radio (NR), integrated access and backhaul (IAB) network is recently envisioned as one of the promising network architectures. In this paper, we propose resource allocation algorithms to increase the backhaul capacity of IAB network and evaluate the performance of IAB network through geometric analysis. First, we propose a centralized resource allocation (CRA) algorithm for the case that IAB donor is available to acquire entire channel state information (CSI) and traffic load information of each IAB node. However, in order to overcome the high propagation loss of mmWave band, a densification of IAB node is necessary, which may induce the high computational complexity and require tremendous amount of CSI feedback load for the CRA scheme. To address this challenge, we discuss the distributed resource allocation (DRA) framework which can be individually operated at the each IAB nodes. On the other hand, we investigate the IAB network to get the insights for the system design by applying geometric analysis. Taking the property of stochastic geometry and hybrid beamforming at mmWave band into account, we derive the tractable approximation of co-channel interference (CCI). This expression allows us to obtain the insights to avoid the effect of CCI. Finally, numerical results demonstrate that our proposed algorithm can improve the performance for IAB network and our geometric analysis is valid.

The Prospect of Reconfigurable Intelligent Surfaces in Integrated Access and Backhaul Networks

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.

Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach based on column generation

Millimeter wave (mmWave) communications have been introduced in the 5G standardization process due to their attractive potential to provide a huge capacity extension to traditional sub-6 GHz technologies. However, such high-frequency communications are characterized by harsh propagation conditions, thus requiring base stations to be densely deployed. Integrated access and backhaul (IAB) network architecture proposed by 3GPP is gaining momentum as the most promising and cost-effective solution to this need of network densification.IAB networks’ available resources need to be carefully tuned in a complex setting, including directional transmissions, device heterogeneity, and intermittent links with different levels of availability that quickly change over time. It is hard for traditional optimization techniques to provide alone the best performance in these conditions. We believe that Deep Reinforcement Learning (DRL) techniques, especially assisted with Long Short-Term Memory (LSTM), can implicitly capture the regularities of environment dynamics and learn the best resource allocation strategy in networks affected by obstacle blockages. In this article, we propose a DRL based framework based on the Column Generation (CG) that shows remarkable effectiveness in addressing routing and link scheduling in mmWawe 5G IAB networks in realistic scenarios.

A Survey on Integrated Access and Backhaul Networks

Benefiting from the usage of the high-frequency band, utilizing part of the large available bandwidth for wireless backhauling is feasible without considerable performance sacrifice. In this context, integrated access and backhaul (IAB) has been proposed by the Third Generation Partnership Project (3GPP) to reduce the expenses related to the deployment of fiber optics for 5G and beyond networks. In this paper, first, a brief introduction of IAB based on the 3GPP release is presented. Then, the existing research on IAB networks based on 3GPP specifications and possible non-3GPP research extensions are surveyed. The research on non-3GPP extensions includes the integration of IAB networks with other advanced techniques beyond the currently defined protocol stacks, such as the integration of IAB to cache-enabled, optical communication transport, and non-terrestrial networks. Finally, the challenges and opportunities related to the development and commercialization of the IAB networks are discussed.

Integrated Access and Backhaul: A New Type of Wireless Backhaul in 5G

In this paper, we study the concept and the potentials of integrated access and backhaul (IAB) as one of the main innovative aspects of 5G networks. We study IAB networks from different perspectives. Particularly, we summarize the recent Rel-16 discussions on IAB, and highlight the main IAB-specific agreements on different protocol layers. Moreover, we present simulations and proof-of-concept testbed results to evaluate the performance of IAB networks in both urban and suburban areas. As show, for both suburban and urban scenarios, IAB is an attractive complement to fiber, with high flexibility and low time-to-market.

Design of Full Duplex Millimeter-Wave Integrated Access and Backhaul Networks

One of the key technologies for the future cellular networks is full duplex (FD)-enabled integrated access and backhaul (IAB) networks operating in the millimeter-wave (mmWave) frequencies. The main challenge in realizing FD-IAB networks is mitigating the impact of self-interference (SI) in the wideband mmWave frequencies. In this article, we first introduce the 3GPP IAB network architectures and wideband mmWave channel models. By utilizing the subarray-based hybrid precoding scheme at the FD-IAB node, multiuser interference is mitigated using zero-forcing at the transmitter, whereas the residual SI after successfully deploying antenna and analog cancellation is canceled by a minimum mean square error baseband combiner at the receiver. The spectral efficiency (SE) is evaluated for the RF insertion loss (RFIL) with different kinds of phase shifters and channel uncertainty. Simulation results show that, in the presence of the RFIL, the almost double SE, which is close to that obtained from fully connected hybrid precoding, can be achieved as compared to half duplex systems when the uncertainties are of low strength.

Optimal Topology Formation and Adaptation of Integrated Access and Backhaul Networks

With the increasing densification of cellular networks, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication coupled with multiple-input-multiple-output (MIMO) and beamforming technologies has opened up the possibility of providing high-speed wireless backhaul to such cell sites. The third-generation partnership project (3GPP) is defining an integrated access and backhaul (IAB) architecture for the fifth-generation (5G) cellular networks, in which the same infrastructure and spectral resources are used for both the access and the backhaul. In IAB networks, SBSs, so-called IAB nodes, act either as relay nodes carrying the traffic through multiple hops from a macrocell to an end user and vice versa or as access points to serve user equipments (UEs) in their proximity. To this end, the topology of such IAB networks is essential to enable efficient traffic flow and minimize congestion or increase robustness to backhaul link failure. In this paper, we propose a topology formation algorithm together with methodologies to implement it in real networks and compare it with a standard random sequence approach as well as with an optimal topology obtained using dynamic programming. Our simulation results demonstrate that the proposed algorithm outperforms the random sequence approach by 26% on average in terms of lower bound of the network capacity and is up to 99.7% close to the optimal solution, while being significantly less complex.

Performance Evaluation of Full-Duplex IAB Multi-Cell and Multi-User Network for FR2 Band

One of the approaches to support high data rates in beyond 5G cellular networks is the dense deployment of the small cell millimeter-wave (frequency range 2 (FR2)) ( $>24.5$ GHz) base stations. However, this dense deployment of base stations leads to high cost, because it requires fiber backhaul connection with the core network. One prominent solution is the integrated access and backhaul (IAB) network proposed in 3GPP Release 16, where some portion of wireless spectrum is used for backhaul to serve base stations instead of fiber, and the remaining portion of the spectrum is used for user equipment for communication. The partition of the spectrum into access and backhaul degrades the spectral efficiency of the IAB network. Thus, in this paper, we propose a full duplex (FD) enabled IAB network with large-scale array systems to enhance the spectral efficiency of the IAB network. In the FD IAB network, the IAB node is FD which transmits and receives at the same frequency/time resources. We consider a multi-cell multi-user IAB network with all the interferences due to FD transmission and evaluate the performance in terms of bit-error-rate and spectral efficiency. Moreover, an algorithm for choosing different users is proposed which is based on the cross-correlation of RF precoder weights. Further, identification of the optimal beam’s index is also proposed which is based on the index of synchronization signal block and half-frame bit of physical broadcast channel payload of 5G NR. Finally, successive interference cancellation is proposed for self-interference mitigation in the digital domain.

Performance Analysis on Cache-Enabled FR2 IAB Networks

This paper brings the concept of wireless edge caching (WEC) to the realm of integrated access and backhaul (IAB) networks. Traditionally, both access and backhaul are allocated fixed spectrum. However, when WEC is applied to the IAB nodes to pre-fetch some popular files, the backhaul traffic can be drastically reduced. Accordingly, an analytical framework is developed for a cache-enabled IAB network for the wide-band frequency range 2 (FR2) (> 24.25 GHz) channel model. In particular, the optimal spectrum resource allocation policy between access and backhaul for IAB networks is investigated by utilizing WEC. For the considered WEC enabled IAB network, a mathematical framework is presented under the content delivery phase for the calculation of three key performance metrics, namely the average success probability (ASP), throughput, and latency of file delivery with respect to various network parameters such as different caching placement strategies, node density, cache size, antenna number, blockage density, and resource partitioning factor. Extensive numerical results are provided to both verify the theoretical derivations and to demonstrate the precedency of using WEC as a cost-effective measure for the improvement of the IAB network’s performance. In particular, under a normalized cache size of 0.6, compared to the baseline no caching scenario, the percentages of backhaul spectrum that can be saved are 48% (26%), 66% (48%), and 47% (27%) when caching the most popular files (caching uniformly) with respect to the ASP, latency, and throughput of file delivery, respectively. The saved backhaul spectrum can then be shifted to the access for its performance enhancements. Our findings also show that there exists an optimal spectrum partition for IAB with respect to different network parameter settings. Further, there exists a tradeoff in the selection of the optimal partitioning factor with respect to ASP/ throughput and latency of file delivery for varying IAB node densities.

Access and Radio Resource Management for IAB Networks Using Deep Reinforcement Learning

Congestion in dense traffic networks is a prominent obstacle towards realizing the performance requirements of 5G new radio. Since traditional adaptive traffic signal control cannot resolve this type of congestion, realizing context in the network and adapting resource allocation based on real-time parameters is an attractive approach. This article proposes a radio resource management solution for congestion avoidance on the access side of an integrated access and backhaul (IAB) network using deep reinforcement learning (DRL). The objective of this article is to obtain an optimal policy under which the transmission throughput of all UEs is maximized under the dictates of environmental pressures such as traffic load and transmission power. Here, the resource management problem was converted into a constrained problem using Markov decision processes and dynamic power management, where a deep neural network was trained for optimal power allocation. By initializing a power control parameter, $\theta _{t}$ , with zero-mean normal distribution, the DRL algorithm adopts a learning policy that aims to achieve logical allocation of resources by placing more emphasis on congestion control and user satisfaction. The performance of the proposed DRL algorithm was evaluated using two learning schemes, i.e., individual learning and nearest neighbor cooperative learning, and this was compared with the performance of a baseline algorithm. The simulation results indicate that the proposed algorithms give better overall performance when compared to the baseline algorithm. From the simulation results, there is a subtle, but critically important insight that brings into focus the fundamental connection between learning rate and the two proposed algorithms. The nearest neighbor cooperative learning algorithm is suitable for IAB networks because its throughput has a good correlation with the congestion rate.

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

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.