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In this paper, we study the problem of joint in-band backhauling and interference mitigation in 5G heterogeneous networks (HetNets) in which a massive multiple-input multiple-output (MIMO) macro cell base station equipped with a large number of antennas, overlaid with self-backhauled small cells is assumed. This problem is cast as a network utility...
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... consider the downlink (DL) transmission of a HetNet scenario as shown in Fig. 1 in which a MBS b 0 is underlaid with a set of uniformly deployed S SCs, S = {b s |s ∈ {1, . . . , S}}. Let B = {b 0 } ∪ S denote the set of all base stations (BSs), where |B| = 1 + S. The MBS is equipped with N number of antennas and serves a set of single-antenna M MUEs M = {1, . . . , M}. Let K = M ∪ S denote the set of b 0 's ...
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Citations
... While massive MIMO with large degrees of freedom provides high energy and spectral efficiency [2], mmWave frequency bands provide large bandwidth [3]. In addition, due to the short wavelength of mmWaves, large antenna array can be packed into highly directional beamforming, which makes massive MIMO practically feasible [4]. Thus far, most of existing works on mmWave-enabled massive MIMO systems focus mainly on providing capacity improvement [4], while latency and reliability are not addressed. ...
... In addition, due to the short wavelength of mmWaves, large antenna array can be packed into highly directional beamforming, which makes massive MIMO practically feasible [4]. Thus far, most of existing works on mmWave-enabled massive MIMO systems focus mainly on providing capacity improvement [4], while latency and reliability are not addressed. Although latency and reliability are applicable to many scenarios (e.g. ...
... Digital Object Identifier 10.1109/LCOMM.2017.2705148 applications), in this work, we are interested in the integration of mmWave communication and massive MIMO techniques, which holds the promise of providing great enhancements of the overall system performance [1], [2], [4]. Specifically, this letter is concerned with addressing the fundamental question in mmWave-enabled massive MIMO systems: "how to simultaneously provide order of magnitude capacity improvements and latency reduction?" ...
With the introduction of new technologies such as Unmanned Aerial Vehicle (UAV), High Altitude Platform Station (HAPS), Millimeter Wave (mmWave) frequencies, Massive Multiple-Input Multiple-Output (mMIMO), and beamforming, wireless backhaul is expected to be an integral part of the 5G networks. While this concept is nothing new, it was shortcoming in terms of performance compared to the fiber backhauling. However, with these new technologies, fiber is no longer the foremost technology for backhauling. With the projected densification of networks, wireless backhaul has become mandatory to use. There are still challenges to be tackled if wireless backhaul is to be used efficiently. Resource allocation, deployment, scheduling, power management and energy efficiency are some of these problems. Wireless backhaul also acts as an enabler for new technologies and improves some of the existing ones significantly. To name a few, rural connectivity, satellite communication, and mobile edge computing are some concepts for which wireless backhauling acts as an enabler. Small cell usage with wireless backhaul presents different security challenges. Governing bodies of cellular networks have standardization efforts going on especially for the Integrated Access & Backhaul (IAB) concept, and this is briefly mentioned. Finally, wireless backhaul is also projected to be an important part of the beyond 5G networks, and newly developed concepts such as cell-free networking, ultra-massive MIMO, and extremely dense network show this trend as well. In this survey, we present the aforementioned issues, challenges, opportunities, and applications of wireless backhaul in 5G, while briefly mentioning concepts related to wireless backhaul beyond 5G alongside with security and standardization issues.
Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.
Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.
Millimeter wave (mmW) self-backhaul has been regarded as a high-capacity and low-cost solution to deploy dense small cell networks, but its performance depends on a resource allocation strategy which can effectively reduce interference (including co-tier interference, cross-tier interference, self-interference). Taking the use of beamforming and the advantage of mmW short-range communication into account, this paper formulates a resource allocation problem, in which sub-channels can be shared among low-interference links, while orthogonal sub-channels can be used at the links that suffer high-level interference among them. The objective is to maximize the sum data rates of all users, while ensuring the data rate of backhaul link at each small cell base station is greater than or equal to the sum data rates of all its served users in the access links. Besides, the data rate of each user should achieve its minimum traffic demand. The optimization problem is a combinatorial integer programming problem with a series of inequality constraints, which is difficult to solve. By introducing penalty function and penalty factors into it, the problem is transferred to an equivalent problem without any inequality, and then it can be addressed by Markov approximation method. By leveraging log-sum-exp method to approximate the equivalent problem, we firstly deduce the near optimal solution. However, it is difficult to calculate the deduced solution since that it needs all possible solution information, and thus a Markov chain is then utilized to converge to the near optimal solution. Numerical results are shown to verify the performance of the proposed algorithm.
Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, this work aims at addressing the fundamental questions: “how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints?”. In this regard, a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: (i) path selection and (ii) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, a comprehensive performance analysis and convergence proof are provided for the proposed solution. Numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99:9999%, and reduces latency by 50:64% and 92:9% as compared to baseline models. Furthermore, the results showcase the key trade-off between latency and network arrival rate.
