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In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless network comprised of two layers of UAVs: (a) a lower layer consisting a number of flying users and a UAV Base Station (BS) that provides coverage for terrestrial users and, (b) an upper layer designated to provide both wireless access for flying users and backhaul connectivity for UAV BS. By adopting IAB technology, the backhaul and access links collaboratively share their resources, enabling aerial backhauling and the utilization of the same infrastructure and frequency resources for access links. A sum-rate maximization problem is formulated by considering aerial backhaul constraints to optimally allocate the frequency spectrum between aerial and terrestrial networks. We decompose the resulting non-convex optimization problem into two sub-problems of beamforming and spectrum allocation and then propose efficient solutions for each. Numerical results in different scenarios yield insightful findings about the effectiveness of using the IAB technique in aerial networks.

Integrated Access and Backhaul (IAB) technology promises to facilitate cost-effective deployments of 5G New Radio (NR) systems operating in both sub-6 GHz and millimeter-wave (mmWave) bands. As full-duplex wireless systems are in their infancy, initial deployments of IAB networks may need to rely on half-duplex operation to coordinate transmissions between access and backhaul links. However, the use of half-duplex operation not only makes the scheduling of links in the IAB networks interdependent, but also the number of their feasible combinations grows exponentially with the network size, thereby posing challenges to the efficient design of such systems. In this paper, by accounting for mmWave radio characteristics, we propose a joint resource allocation and link scheduling framework to enhance the user equipment (UE) throughput in multi-hop in-band IAB systems. We keep the problem in the form of linear programming type for the feasibility of the practical applications. We show that the increased number of uplink and downlink transmission time interval (TTI) configurations does not result in improved UE throughput as compared to two-TTI configuration. Further, we demonstrate that in-band IAB systems tend to be backhaul-limited, and the utilization of multi-beam functionality at the IAB-donor alleviates this limitation by doubling the average UE throughput. Finally, we show that the use of proportional-fair allocations allows the average UE throughput to be improved by around 10% as compared to the max-min allocations.

Fifth Generation (5G) mobile networks considers an expansive set of heterogeneous services with stringent Quality of Service (QoS) requirements, and traffic demand with inherent spatial-temporal distribution, which places the backhaul network deployment under potential strain. In this paper, we propose to harness network slicing, Integrated Access and Backhaul (IAB) technology coupled with satellite connectivity to build a dynamic wireless backhaul network that can provide additional backhaul capacity to the base stations on demand when the wired backhaul link is temporarily out of capacity. To construct the network design, Deep Reinforcement Learning (DRL) models are used to select, for each network slice of the congested base station, an appropriate backhaul link from the pool of available IAB and satellite links that meets the QoS requirements (i.e., throughput and latency) of the slice. Simulation results show that around 20 episodes are sufficient to train a Double Deep Q-Network (DDQN) agent, with one fully-connected hidden layer and Rectified Linear Unit (ReLU) activation function, that adjusts the topology of the backhaul network.

This study analyzes the performance of 3rd Generation Partnership Project (3GPP)-inspired multi-cell wideband single-hop backhaul millimeter-wave-in-band-full-duplex (IBFD)-integrated access and backhaul (IAB) networks using stochastic geometry. We modeled the wired-connected Next Generation NodeBs (gNBs) as the Matérn hard-core point process (MHCPP) to meet real-world deployment requirements and reduce the cost caused by wired connections in the network. We first derive association probabilities that reflect the likelihood that typical user-equipment is served by a gNB or an IAB-node based on the maximum long-term averaged biased-received-desired-signal power criteria. Furthermore, by leveraging the composite Gamma-Lognormal distribution, we derived the closed-form signal to interference plus noise ratio coverage, capacity with outage, and ergodic capacity of the network. To avoid underestimating the noise, we consider the sidelobe gain on the inter-cell interference links and analog-to-digital converter quantization noise. Compared with half-duplex transmission, the numerical results show an enhanced capacity with outage and ergodic capacity provided by the IBFD under successful self-interference cancellation. We also study how the power bias and density ratio of the IAB-node to gNB and the hard-core distance can affect system performance.

Integrated access and backhaul (IAB) networks operating in full-duplex (FD) mode at millimeter wave frequencies have been actively investigated in the context of future-generation communications networks. However, conventional analog cancellation techniques cannot adequately mitigate the self-interference resulting from the FD operation and the multi-user interference. Hence, in this paper, we consider a multi-cell, multi-user IAB network and jointly design the beamforming and combining matrices to maximize the network’s weighted sum rate. Given the non-convex nature of the problem, we reformulate it using weighted minimum-mean-square-error (WMMSE) and extended fractional programming (FP) techniques followed by a block coordinate descent (BCD) approach. Extensive simulation results validate the superior performance of our proposed algorithms. Specifically, the WMMSE and FP methods can achieve 50 bits/sec/Hz higher than the benchmark scheme for a network employing three cells with two uplink and two downlink users per cell.

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. [ABSTRACT FROM AUTHOR]

In view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed.Firstly, for the integrated access backhaul system deployed in smart grid, considering the different communication requirements of services in lightweight and non-lightweight terminal, the spectrum allocation problem was formulated as a non-convex mixed-integer programming aiming to maximize the overall energy efficiency.Secondly, the above problem was modeled as a partially observable Markov decision process and transformed into a fully cooperative multi-agent problem, then a spectrum allocation algorithm was proposed which was based on multi-agent proximal policy optimization under the framework of centralized training and distributed execution.Finally, the performance of the proposed algorithm was verified by simulation.The results show that the proposed algorithm has a faster convergence speed and can increase the overall transmission rate by 25.2% through effectively reducing intra-layer and inter-layer interference and balancing the access and backhaul link rates.

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In this paper, we analyze the spectral efficiency performance of various transmission schemes in an integrated access and backhaul (IAB) network with multiple backhaul links, where the interference between links is considered assuming that the backhaul and access links use the same frequency band. As the end-to-end transmission schemes, we present the transmission schemes using non-orthogonal multiple access (NOMA) and orthogonal multiple access, respectively, and the transmission scheme using no multiple access. Assuming the path loss and Rayleigh fading channel at each link, we derive the expressions for the spectral efficiency of the three transmission schemes, and using Monte Carlo simulation, we compare the average spectral efficiency of the transmission schemes. Through the simulation results, we show that for low signal-to-noise power ratio (SNR), the transmission schemes using multiple access achieve better performance than that using no multiple access, but for high SNR, the transmission scheme using no multiple access provides superior performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a load-balancing algorithm for small-cell integrated access and backhaul (IAB) networks operating in the millimeter wave (mmWave) band. With the help of mmWave communications, ultra-dense small cell deployment is a key technology for future networks, but it leads to high fiber installation costs. IAB has emerged as a cost-effective and flexible solution because it uses the multi-hop wireless backhaul to the core network via macro base stations (BSs). Multi-hop transmission and spectrum sharing between access and backhaul links cause an unbalanced load across BSs under IAB. To tackle the unbalanced load problem, we propose a graph-based load balancing algorithm for IAB by estimating and adapting to network load status. The weight of the graph is represented as the cost of the link between two BSs and is defined to reflect the cell load and link capacity. The proposed algorithm estimates the upcoming load and adjusts the network transmission schedule to balance the load inside the network. Through simulations, performance is evaluated in various environments. The simulation results show that the proposed algorithm not only distributes the load across small cells more evenly but also increases network throughput by 22.3% and 32.1% with 16.3% and 28.7% higher in UEs satisfaction when compared to MaxMinThroughput and Backpressure algorithms.

An integrated access and backhaul (IAB)-enabled small-cell network commonly utilizes frequency channels for access and backhaul links, and thus this network has a chance to utilize the frequency channels efficiently and optimally. However, there are still several problems with applying the IAB technology to practical small-cell networks, such as extremely high computational complexity caused by shared resource utilization and additional co-tier and cross-tier interference management. Therefore, we herein propose a multi-agent distributed Q-learning with pre-resource partitioning (MADQ-PRP) algorithm to solve the problem of frequency channel allocation and energy consumption. In MADQ-PRP, to reduce the computational complexity, each RL agent only considers its local state information to determine its following action. Nevertheless, by sharing and redistributing the rewards among agents, the overall reward can be maximized. Furthermore, we devise a pre-resource partitioning method depending on the variations in the number of SBSs per MBS and the numbers of MBS and SBS channels to reduce the computational complexity of the proposed MADQ-PRP algorithm. Through intensive simulations, we show the convergence of the proposed MADQ-PRP algorithm to the optimal solution obtained by the exhaustive search algorithm. Also, we demonstrate that the proposed MADQ-PRP algorithm outperforms several benchmark algorithms such as ‘Random action,’ ‘SBS on-off,’ ‘SBS-only,’ and ‘MADQ-only’ in IAB-enabled small-cell networks with non-uniform traffic distribution. Furthermore, it is confirmed that the proposed MADQ-PRP algorithm can reduce the CPU execution time by 9.1% and 97.9% compared to the distributed and centralized RL algorithms, respectively. The proposed algorithm based on the low-complexity RL and PRP could be one of the solutions to optimize the heterogeneous network performance from the perspective of the network operators when considering the coverage-capacity tradeoff.

With the increasing scarcity of spectrum resources and the continuous exploration of high-frequency spectrum resources, millimeter wave (mmWave) has been introduced to 5G deployment scenarios, one of which is the high-speed railway wireless networks. Because of the characteristics of mmWave, it is suitable for Enhanced Mobile Broadband (eMBB) service in subway tunnel scenarios, in which there are increasing data rate requirement of passengers to be satisfied. To this end, we propose a new scheme of Integrated Access and Backhaul (IAB) plus small stations in the carriage, in which the backhaul link is 5G Non-Standalone (NSA) networking with 4G for master node and 5G mmWave for secondary node, so as to overcome the handover burden and penetration loss of mmWave, that is more severe in the high-frequency range. Based on the existing old 4G equipment, mmWave is applied for 5G user-plane connection in NSA dual connection, while the control plane connection still relies on 4G commercial frequency band, in order that even if the millimeter-wave link quality is seriously degraded, the IAB-node can still maintain the connection with the master node and re-establish the mmWave link through the exchange of control information on the 4G low-frequency band. Unlike previous attempts, the proposed approach applies dual link to the backhaul link and 5G commercial frequency band to the in-vehicle access link of small station with Radio Access Network (RAN) sharing technology, so that the bandwidth shared by the operators can be aggregated in one small station and fully used for user access to match the mmWave large bandwidth of the backhaul link. Field trials showed that although the data rates were somewhat unstable at the handover points, which still need to be improved in the future work, the average end-to-end uplink and downlink data rates were greatly improved, compared to the previous average data rates when only outdoor signal penetrating indoors was used in subway tunnel. In addition, the proposed scheme can reduce the operator’s construction and maintenance costs by making full use of the existing 4G equipment, which provides a reference for upgrading 4G to 5G signal coverage for all tunnel scenarios.

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) was proposed and implemented in a knowledge-defined networking architecture of an IAB network. The incorporation of the RDCM was shown to improve robustness to backhaul failure in IAB networks. 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 and the results showed that the proposed algorithm provides better throughput and delay performance over the two baselines.

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.

We investigate propagation characteristics for wireless channels, applicable to Integrated Access and Backhaul (IAB) and relay-based networks with lamppost-height nodes at 5.5 GHz. We compare our empirical results with a variety of models that have been proposed for system simulation. Our work is based on an extensive measurement campaign in an urban environment, where we simultaneously measured base-relay, relay-mobile and base-mobile links. This simultaneity allows us to conclude that low-height relay nodes offer a minor path-loss advantage over the base-user link. Moreover, within the range of relay heights that we measured of 2.8 and 4.7 m, we observed no signi?cant gain associated with choosing the higher relay placement. Our results however also show that the base-relay link is quite stable over time and thus will lend itself to multi-antenna techniques requiring a small overhead in channel state information feedback. Our results add to the empirical data that the standards models are based on, providing path-loss results obtained simultaneously for all links of an urban relay-based system.

This paper describes a 26-GHz fully integrated In-band Full-duplex Circulator (IBFD) Receiver (RX), which employs passive and active Self-interference Cancellation (SIC) techniques in the mm-wave domain. Coverage of wireless networks at mm-wave frequencies can be enhanced by deploying a large number of base stations economically based on Integrated Access and Backhaul (IAB) relays and repeaters. However, to retain the channel capacity, IAB needs to be implemented using full-duplex schemes that suffer from a strong Transmitter (TX) to RX SI. This SI leakage can significantly impact the receiver sensitivity and increase the baseband/ADC dynamic range requirements. Canceling SI at mm-wave applications is particularly challenging given the high frequency of operation, wide bandwidth, and antenna (ANT) impedance sensitivity to the surroundings. Proposed mm-wave RX with a shared ANT interface based on a circulator with active SI cancelers provide ~53 dB SIC over 400 MHz and ~40 dB SIC over 400 MHz in mm-wave domain to meet the link budget requirements. Proposed architecture achieves SIC by (i) introducing a shared ANT interface based on a hybrid-coupler and a non-reciprocal delay line that provides wideband SIC and additionally creates a SI replica (ii) subsequent active cancellation using SI replica processed with variable gain amplifiers and phase shifters. This system also accommodates SI channel variations due to surroundings. Proposed 26-GHz circulator RX has >100x better SIC at high TX power (>10dBm) levels in comparison to the state-of-the-art and it consumes ~111 mW power. The system is implemented in 45nm SOI CMOS and has an active area of $4.54\, mm^{2}$ . Stand-alone RX NF is ~5.8 dB and TX to ANT insertion loss (IL) is ~3.1 dB. Over-the-air measurements with modulated TX (128 QAM 2.1 Gb/s) and RX (128 QAM 4.2 Gb/s) signals show an EVM of 3.3%.

We present measurements and analysis of self-interference in multi-panel millimeter wave (mmWave) full-duplex communication systems at 28 GHz. In an anechoic chamber, we measure the self-interference power between the input of a transmitting phased array and the output of a colocated receiving phased array, each of which is electronically steered across a number of directions in azimuth and elevation. These self-interference power measurements shed light on the potential for a full-duplex communication system to successfully receive a desired signal while transmitting in-band. Our nearly 6.5 million measurements illustrate that more self-interference tends to be coupled when the transmitting and receiving phased arrays steer their beams toward one another but that slight shifts in steering direction (on the order of one degree) can lead to significant fluctuations in self-interference power. We analyze these measurements to characterize the spatial variability of self-interference to better quantify and statistically model this sensitivity. Our analyses and statistical results can be useful references when developing and evaluating mmWave full-duplex systems and motivate a variety of future topics including beam selection, beamforming codebook design, and self-interference channel modeling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper develops a 3GPP-inspired design for the in- band-full-duplex (IBFD) integrated access and backhaul (IAB) networks in the frequency range 2 (FR2) band, which can enhance the spectral efficiency (SE) and coverage while reducing the latency. However, the self-interference (SI), which is usually more than 100 dB higher than the signal-of-interest, becomes the major bottleneck in developing these IBFD networks. We design and analyze a subarray-based hybrid beamforming IBFD-IAB system with the RF beamformers obtained via RF codebooks given by a modified Linde-Buzo-Gray (LBG) algorithm. The SI is canceled in three stages, where the first stage of antenna isolation is assumed to be successfully deployed. The second stage consists of the optical domain (OD)-based RF cancellation, where cancelers are connected with the RF chain pairs. The third stage is comprised of the digital cancellation via successive interference cancellation followed by minimum mean-squared error baseband receiver. Multiuser interference in the access link is canceled by zero-forcing at the IAB-node transmitter. Simulations show that under 400 MHz bandwidth, our proposed OD-based RF cancellation can achieve around 25 dB of cancellation with 100 taps. Moreover, the higher the hardware impairment and channel estimation error, the worse digital cancellation ability we can obtain. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of topology optimization and routing in integrated access and backhaul (IAB) networks, as one of the promising techniques for evolving 5G networks. We study the problem from different perspectives. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution, and evaluate the effect of routing on bypassing temporal blockages. Here, concentrating on millimeter wave-based communications, we study the service coverage probability, defined as the probability of the event that the user equipments’ (UEs) minimum rate requirements are satisfied. Moreover, we study the effect of different parameters such as the antenna gain, blockage, and tree foliage on the system performance. Finally, we summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on routing in IAB networks, and discuss the main challenges for enabling mesh-based IAB networks. As we show, with a proper network topology, IAB is an attractive approach to enable the network densification required by 5G and beyond.

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.

The next generations of mobile networks will be deployed as ultra-dense networks, to match the demand for increased capacity and the challenges that communications in the higher portion of the spectrum (i.e., the mmWave band) introduce. Ultra-dense networks, however, require pervasive, high-capacity backhaul solutions, and deploying fiber optic to all base stations is generally considered to be too expensive for network operators. The 3rd Generation Partnership Project (3GPP) has thus introduced Integrated Access and Backhaul (IAB), a wireless backhaul solution in which the access and backhaul links share the same hardware, protocol stack, and also spectrum. The multiplexing of different links in the same frequency bands, however, introduces interference and capacity sharing issues, thus calling for the introduction of advanced scheduling and coordination schemes. This paper proposes a semi-centralized resource allocation scheme for IAB networks, designed to be flexible, with low complexity, and compliant with the 3GPP IAB specifications. We develop a version of the Maximum Weighted Matching (MWM) problem that can be applied on a spanning tree that represents the IAB network and whose complexity is linear in the number of IAB-nodes. The proposed solution is compared with state-of-the-art distributed approaches through end-to-end, full-stack system-level simulations with a 3GPP-compliant channel model, protocol stack, and a diverse set of user applications. The results show that our scheme can increase the throughput of cell-edge users up to 3 times, while decreasing the overall network congestion with an end-to-end delay reduction of up to 25 times. [ABSTRACT FROM AUTHOR]

In this article, we introduce and study the potentials and challenges of integrated access and backhaul (IAB) as one of the promising techniques for evolving 5G networks. We study IAB networks from different perspectives. We summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on IAB, and highlight the main IAB-specific agreements on different protocol layers. Also, concentrating on millimeter wave-based communications, we evaluate the performance of IAB networks in both dense and suburban areas. Using a finite stochastic geometry model, with random distributions of IAB nodes as well as user equipments (UEs) in a finite region, we study the service coverage rate defined as the probability of the event that the UEs' minimum rate requirements are satisfied. We present comparisons between IAB and hybrid IAB/fiber-backhauled networks where a part or all of the small base stations are fiber-connected. Finally, we study the robustness of IAB networks to weather and various deployment conditions and verify their effects, such as blockage, tree foliage, rain as well as antenna height/gain on the coverage rate of IAB setups, as the key differences between the fiber-connected and IAB networks. As we show, IAB is an attractive approach to enable the network densification required by 5G and beyond.

Integrated access and backhaul (IAB) network consists of base station (BS), relay nodes (RNs), and user-equipments (UEs), where BS and RNs exchange UE data via wireless in-band backhaul while sharing the same frequency-time resources with access links. In this paper, a flexible time-division-duplex (TDD)-based IAB network is considered where RNs and BS are assigned to distinct uplink (UL) or downlink (DL) transmission modes to mitigate conventional half-duplex (HD) loss at RNs. An iterative beamformer design is proposed to manage the resulting cross-channel interference and to allocate wireless backhaul and access resources jointly over two consecutive data delivery intervals required for communications between the BS and UEs through HD RNs. Dynamic traffic behavior is handled via weighted queue minimization objective, and user-specific UL/DL queues are also introduced at RNs to guarantee reliable end-to-end data delivery. Bi-directional forward-backward training via spatially precoded over-the-air pilot signaling is employed to allow decentralized beamformer design across all the nodes. A novel user allocation method is proposed to assign UEs to BS or RNs based only on long-term channel statistics and some practical IAB limitations. The numerical examples illustrate the superior system performance of the considered flexible IAB in comparison to the conventional HD relaying system.

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++.

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.

As the density of cellular networks grows, it becomes exceedingly difficult to provide traditional fiber backhaul access to each cell site. Millimeter wave communication coupled with beamforming can be used to provide high-speed wireless backhaul to such cell sites. Therefore, the 3rd generation partnership project (3GPP) defines an integrated access and backhaul (IAB) architecture, in which the same infrastructure and spectral resources are shared to provide access and backhaul. However, this complicates the design of topologies in such networks as they need to enable efficient traffic flow and minimize congestion or increase robustness to backhaul link failure. We formulate this problem as a graph optimization problem that maximizes the lower bound of the network capacity and propose a topology formation approach based on a combination of deep reinforcement learning and graph embedding. Our proposed approach is significantly less complex, more scalable, and yields very close performance compared to the optimal dynamic programming approach and significant gains when compared with baseline approaches. [ABSTRACT FROM AUTHOR]

With the advent of 5G networks means for backhauling network traffic emerged as a major consideration. Densification via small cell deployment has made wired solution cost prohibitive, leading to the adoption of spectrally efficient wireless solutions, namely, Integrated Access and Backhaul (IAB). This paper discusses the different options for IAB and focuses on novel solutions for in-band IAB using Full Duplex (FD) enabled nodes. Dynamically reconfigured beam steering antennas are presented as a means of passively suppressing Self-Interference (SI). Experimental results confirm up to 65 dB of SI cancellation in a harsh environment with obstacle. Finally, robust methods for managing FD practical considerations including timing and frequency offsets are presented, with experimental results confirming performance. [ABSTRACT FROM AUTHOR]

As millimeter wave (mmWave) beamforming (BF) system can increase the areal capacity in ultra-dense network (UDN), a joint design of user association and hybrid BF is challenging due to the high feedback overhead and complicated interference management where many communication nodes use large antenna arrays. In addition, the wireless backhaul should be incorporated because it allows a flexible design, while the wired backhaul is difficult to be installed as the number of small base stations increases. To integrate the wireless backhaul and access, the time resource partitioning between the backhaul and access transmissions is needed, which affects the network performance combined with the user association and BF design. In this regard, this paper deals with the joint problem of the time resource allocation, user association, and BF design to maximize the weighted sum rate. Then a two-stage design approach is proposed based on the limited channel feedback to reduce the complexity and overhead. Simulation results show that the proposed limited feedback hybrid BF scheme outperforms the baseline schemes. In addition, we find that the maximum spatial multiplexing gain can be achieved if the number of antennas of each node increases with an order of larger than the square of the number of RF chains. At the same time, the multi-node diversity gain can be obtained in mmWave UDN, which depends on the path loss exponent of line-of-sight. [ABSTRACT FROM AUTHOR]

With the emergence of integrated access and backhaul (IAB) in the fifth generation (5G) of cellular networks, backhaul is no longer just a passive capacity constraint in cellular network design. In fact, this tight integration of access and backhaul is one of the key ways in which 5G millimeter wave (mm-wave) heterogeneous cellular networks (HetNets) differ from traditional settings where the backhaul network was designed independently from the radio access network (RAN). With the goal of elucidating key design trends for this new paradigm, we develop a stochastic geometry-based analytical framework for a millimeter wave (mm-wave) two-tier HetNet with IAB where only the macro BSs (MBSs) have fiber access to the core network and the small cell BSs (SBSs) are wirelessly backhauled by the MBSs over mm-wave links. For this network, we derive the downlink rate coverage probability for two types of resource allocations at the MBS: 1) integrated resource allocation (IRA): where the total bandwidth (BW) is dynamically split between access and backhaul, and 2) orthogonal resource allocation (ORA): where a static partition is defined for the access and backhaul communications. Our analysis concretely demonstrates that offloading users from the MBSs to SBSs may not provide similar rate improvements in an IAB setting as it would in a HetNet with fiber-backhauled SBS. Our analysis also shows that it is not possible to improve the user rate in an IAB setting by simply densifying the SBSs due to the bottleneck on the rate of wireless backhaul links between MBS and SBS. [ABSTRACT FROM AUTHOR]

With the increasing network densification, 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 (mm-wave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mm-wave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which its downlink rate coverage probability is accurately characterized. Using this framework, we study the performance of three backhaul bandwidth (BW) partition strategies: 1) equal partition: when all SBSs obtain equal share of the backhaul BW; 2) instantaneous load-based partition: when the backhaul BW share of an SBS is proportional to its instantaneous load; and 3) average load-based partition: when the backhaul BW share of an SBS is proportional to its average load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs. [ABSTRACT FROM AUTHOR]

To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, bothwireless access and backhaul linksare envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio technology is considering aflexibleandreconfigurablebackhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g.,unmanned aerial vehicles(UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: 1) realistic 3-D multipath mmWave propagation; 2) dynamic blockage of mmWave backhaul links; and 3) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular. [ABSTRACT FROM AUTHOR]