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With the explosive growth of mobile data demand, there has been an increasing
interest in deploying small cells of higher frequency bands underlying the
conventional homogeneous macrocell network, which is usually referred to as
heterogeneous cellular networks, to significantly boost the overall network
capacity. With vast amounts of spectrum avail...
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... high speed wireless backhaul is more cost-effective, flexible, and easier to deploy [3], [19], [20], [21]. Wireless backhaul in mmWave bands, especially 60 GHz band, can be a promising backhaul solution for small cells [20]. The wide bandwidth enables several-Gbps data rate even with simple modulation schemes such as OOK, BPSK and FSK. In Fig. 1, we present a typical scenario for dense deployment of small cells in the 60 GHz band underlying the macrocell cellular network, which forms HCNs. In HCNs, the macrocell is coupled with the small cells in the mmWave band to some extent [22], and the macrocell network is allowed to have certain visibility and radio resource management ...
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... further plot the average flow throughput of four pro- tocols for IPP traffic under different traffic loads in Fig. 9. is set to 4. In Fig. 10, we plot the network throughput of four protocols with different number of WNs. From the results, we can observe that D2DMAC outperforms the other three protocols significantly. The network throughput of D2DMAC increases with the number of WNs. The reason is that with the increase of WN, the WN distribution density increases, and the ...
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... Furthermore, the transmission rates of the direct transmission paths also in- crease due to shorter link length. Thus, the network throughput of D2DMAC increases when the number of WNs increases. As stated before, the gap between D2DMAC and FDMAC-E also indicates the advantages of joint scheduling of access and backhaul networks in D2DMAC. In Fig. 11, we also plot the average flow throughput of four protocols under different number of WNs. From the results, we can observe that they are consistent with those in Fig. 10, which demonstrates D2DMAC has better utilization of the device-to-device transmissions to improve network perfor- mance under the user densely distributed scenario. ...
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... the number of WNs increases. As stated before, the gap between D2DMAC and FDMAC-E also indicates the advantages of joint scheduling of access and backhaul networks in D2DMAC. In Fig. 11, we also plot the average flow throughput of four protocols under different number of WNs. From the results, we can observe that they are consistent with those in Fig. 10, which demonstrates D2DMAC has better utilization of the device-to-device transmissions to improve network perfor- mance under the user densely distributed scenario. Combining the results of Fig. 5 (a), we have demonstrated that D2DMAC has superior performance in the user densely distributed sce- nario of the next generation mobile ...
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... different path selection parameters. We investigate four cases, with β equal to 1, 2, 3, 4, and 5 respectively. For simplicity, we denote these cases by D2DMAC-1, D2DMAC-2, D2DMAC-3, D2DMAC-4, and D2DMAC-5. 1) Delay: We then evaluate the average transmission delay of D2DMAC with different path selection parameters under different traffic loads in Fig. 12. From the results, we can observe that with the increase of traffic load, the delay of these protocols increases, and β has a big impact on the performance of D2DMAC. As we can observe, D2DMAC- 2 achieves the best delay performance. When β decreases from 5 to 2, the delay becomes better. However, when β is equal to 1, its delay becomes ...
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... the backhaul network usually have multiple hops, which may increase the probability of concurrent transmissions for hops from different flows. Therefore, in practice, β should be optimized according to the actual network states and settings. We also plot average flow delay of D2DMAC with different path selection parameters under IPP traffic in Fig. 13. From the results, we can observe that D2DMAC with β equal to 2 outperforms D2DMAC in other cases for flows both between WNs and from or to the Internet. Therefore, the path selection parameter should be optimized to achieve low flow ...
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... Throughput: We plot the network throughput of D2DMAC with different different path selection parameters under different traffic loads in Fig. 14. We can observe that the throughput is consistent with the delay in Fig. 12 traffic load, throughput increases with the traffic load. When the traffic load exceeds some critical point, which is different for different β, the throughput starts to decrease. For example, the critical point for D2DMAC-3 is 4. This can be explained as ...
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... Throughput: We plot the network throughput of D2DMAC with different different path selection parameters under different traffic loads in Fig. 14. We can observe that the throughput is consistent with the delay in Fig. 12 traffic load, throughput increases with the traffic load. When the traffic load exceeds some critical point, which is different for different β, the throughput starts to decrease. For example, the critical point for D2DMAC-3 is 4. This can be explained as follows. Delay increases with the traffic load, and when traffic load exceeds the ...
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Citations
... 6G networks must also tackle the increasing spectrum scarcity and security concerns due to increasing mobility. Niu et al. [75] have discussed the main communication issue in Joint Scheduling of access and backhaul for mmWave small cells is the limited availability of resources, such as radio spectrum and infrastructure, as well as the limited range of mmWave communication. It is difficult for the network to effectively utilize the available resources for transmitting data both over the air and through the backhaul. ...
The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.
... For the mmWave HetNets, the performance of the system is influenced by both the access link and the backhaul link, so it is important to jointly design both of them [10]. Specifically, there are two vital problems to be solved. ...
... Data transmission is carried out between the MBS and SBSs through mmWave backhaul links. We assume that the MBS and SBSs can be appropriately deployed to avoid the blockage of the backhaul link [10]. Thus, the stable line-of-sight (LOS) connection can be established between the MBS and each SBS without relaying through other SBSs. ...
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.
... With the fast development of 5G new radio technologies, intensive research on enhanced mobile broadband (eMBB), massive machinetype communications (mMTCs), and ultrareliable low latency communications (URLLCs) have gotten a lot of interest from academics and industry [4]. To satisfy the 5G outlook, it is necessary not only to achieve significant improvements in new wireless technologies but also to take into account the harmonious and fair coexistence of heterogeneous networks and compatibility between 4G and 5G systems [5]. As shown in Figure 2, the unprecedented growth of the IoT creates an enormous demand for MTC, which can be divided into three categories: 1) short-distance MTC (distance = 10 m), 2) medium-distance MTC (distance ranges between [10 m, 100 m]), and 3) long-distance MTC (distance ≥ 100 m). ...
Narrow-band Internet of Things (NB-IoT) is a low-power wide-area network (LPWAN) method that was first launched by the 3rd generation partnership project (3GPP) Rel-13 with the purpose of enabling low-cost, low-power, and wide-area cellular connections for the Internet of Things (IoT). As the demand for over-the-air services grows and with the number of linked wireless devices reaching 100 billion, wireless spectrum is becoming scarce, necessitating creative techniques that can increase the number of connected devices within a restricted spectral resource to satisfy service needs. Consequently, it is vital that academics develop efficient solutions to fulfill the quality of service (QoS) criteria of the NB-IoT in the context of 5th generation (5G). This study paves the way for 5G networks and beyond to have increased capacity and data rates for NB-IoT. Whereas, this article suggests a method for increasing the number of connected devices by using a technique known as symbol time compression (STC). The suggested method reduces the occupied bandwidth of each device without increasing complexity, losing data throughput, or affecting bit error rate (BER) performance. The STC approach is proposed in the literature to work with conventional orthogonal frequency-division multiplexing (OFDM) to reduce bandwidth usage by 50% and enhance the peak-to-average power ratio (PAPR). Specifically, an STC-based technique is suggested that exploits the available bandwidth to increase the number of linked devices by double while keeping the complexity and performance of the system. Furthermore, the µ-law companding technique is leveraged to reduce the PAPR of the transmitted signals. The obtained simulation results indicate that the suggested method using the µ-law companding technique doubles the amount of transferred data and lowers the PAPR by 3.22 dB while keeping the same complexity and BER.
... Niu et al. [18] propose a centralized MAC (medium access control) scheduling scheme that enables device-todevice (D2D) communications aiming for performance improvement. The communication system simulated in [18] utilizes mmWaves, small cells, MIMO (multiple input multiple output) antennas, and beamforming. ...
... Niu et al. [18] propose a centralized MAC (medium access control) scheduling scheme that enables device-todevice (D2D) communications aiming for performance improvement. The communication system simulated in [18] utilizes mmWaves, small cells, MIMO (multiple input multiple output) antennas, and beamforming. Different from ours, the authors propose to solve the problem via NLP (nonlinear programming) instead of a machine learning-based approach. ...
In this work, an adaptive resource allocation algorithm based on reinforcement learning is proposed for multicarrier communication systems that consider multiple users and multipath channel characteristics assuming propagation of millimeter waves. An adaptive Markovian model represented by the queueing states in buffers and the channel states is proposed to describe the Cyclic Prefix—Orthogonal Frequency Division Multiplexing (CP-OFDM) communication system. Novel utility functions for the Markovian model-based Q-learning algorithm are introduced and evaluated. The performance of the proposed adaptive resource allocation scheme based on Markovian model reinforcement learning is verified via computational simulations considering real traffic traces. The simulation results show that the application of the proposed resource scheduling algorithm provides general improvements in the communication system performance parameters such as increased throughput and decreased packet loss when using the reward function proposals and increased energy efficiency for one of the proposed reward functions when compared to some algorithms present in the literature. Simulation results confirm that the proposed reward functions in conjunction with the Markov model make the scheduling of users and the sharing of resources more efficient for millimeter-wave-based CP-OFDM networks than traditional Q-learning-based algorithms.
... The investigations on the transmission scheduling or resource allocation schemes to suppress interference and boost concurrent transmissions in mmWave D2D communication systems have attracted much interest [12][13][14][15][16][17]. ...
... The work in [12] theoretically proved that introducing D2D transmissions can improve the coverage and spectral efficiency of mmWave communication systems. The authors in [13] proposed a novel D2D path selection method for mmWave small cell systems to improve delay and throughput performance effectively. In [14], the user clustering, beamforming design, and power allocation methods for D2D communications underlaying cellular networks were proposed, which can reduce the interference level and improve spectral efficiency. ...
The symbiosis of millimeter wave (mmWave) transmission and Device-to-Device (D2D) communication has become a a promising candidate for temporary or spontaneous short-distance communications. Although the use of directional antennas in mmWave communications can effectively reduce the interference between users, as the network becomes much denser, the mmWave D2D communication network tends to be interference-limited rather than noise-limited. Significantly, the inter-user interference from the line-of-sight path should be carefully circumvented to boost the achievable rate further. We first identify that beamwidth, channel state, and resource allocation jointly affect the interference level and achievable rate. To effectively manage the interference, a joint beamwidth selection and resource optimization problem are established to maximize the sum rate. We propose a two-stage algorithm to solve this non-convex mixed integer programming with low complexity. First, a feasibility-aware particle swarm optimization based beamwidth selection algorithm is proposed to find the proper beamwidth. Second, a constrained concave-convex procedure based joint power optimization and time slots scheduling algorithm is proposed to facilitate the concurrent transmission and further improve the sum rate. Numerical results demonstrate that the proposed two-stage algorithm can effectively reduce interference and improve the sum rate with low complexity.
... The basic idea of dynamic scheduling (DS) is illustrated in Fig. 2. The overall time consumption is composed of a scheduling stage and a transmission stage, while the scheduling time can be amortized over multiple scheduling operations in the transmission phase [35], [36]. The transmission time composes of multiple time slots, and multiple no-conflicting links can be transmitted concurrently in the same time interval. ...
... This is a mixed integer nonlinear programming (MINLP) problem due to the discrete nature of flow scheduling with QoS constraints. Generally, such a problem is NP-hard [36]. Considering the difficulty in solving it directly, we propose two heuristic algorithms as the practical solutions to this NP-hard problem. ...
To deal with the explosive growth of mobile traffic, millimeter-wave (mmWave) communications with abundant bandwidth resources have been applied to vehicular networks. As mmWave signal is sensitive to blockage, we introduce the unmanned aerial vehicle (UAV)-aided two-way relaying system for vehicular connection enhancement and coverage expansion. How to improve transmission efficiency and to reduce latency time in such a dynamic scenario is a challenging problem. In this paper, we formulate the joint optimization problem of relay selection and transmission scheduling, aiming to reduce transmission time while meeting the throughput requirements. To solve this problem, two schemes are proposed. The first one is the random relay selection with concurrent scheduling (RCS), a low-complexity algorithm implemented in two steps. The second one is the joint relay selection with dynamic scheduling (JRDS), which fully avoids relay contentions and exploits potential concurrent ability, to obtain further performance enhancement over RCS. Through extensive simulations under different environments with various flow numbers and vehicle speeds, we demonstrate that both RCS and JRDS schemes outperform the existing schemes significantly in terms of transmission time and network throughput. We also analyze the impact of threshold selection on achievable performance.
... There exist several studies on how to trade-off throughput for fairness using different approaches to the service architecture, which invoke the coordination of cellular and D2D transmissions. [17] and [18], for example, propose to apply user association and D2D multi-hop offloading, respectively. The authors of [6] and [19] implemented an opportunistic relay on WiFi Direct on a millisecond timescale. ...
... In B5G cellular networks enabling D2D communication, several scenarios are introduced within each cell to mitigate interference. However, novel IM strategies must be suggested that consider directional interference in B5G cellular networks to support a wide range of D2D communication [176]. On the other hand, IM is one of the main issues when D2D technology is integrated with SCs in underlay in-band D2D communication. ...
Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.
... Transmission rates reaching 8 Gbps [55] and 176 Gbps [19,67] are observed within a 60 GHz frequency band in several scenarios. Moreover, mmWave is envisioned as a low-cost substitute for expensive fiber optic backhaul in cellular networks [32,59], without compromising the multigigabit transmission rates [44]. mmWave signals suffer notable propagation losses when penetrating buildings or solid materials like walls. ...
As global cellular networks converge to 5G, one question lingers: Are we ready for the 5G challenge? A growing concern surrounds how well do existing congestion control algorithms perform in diverse 5G networks. Given that 5G networks are not yet widely deployed, assessing the performance of existing congestion control algorithms in realistic 5G settings presents several challenges. Moreover, existing network simulation and emulation environments are also not ideally suited to address the unique challenges of 5G network environments. Therefore, building a simple and easily accessible platform becomes crucial to allow testing and comparison of congestion control algorithms under different testing conditions. This paper makes two main contributions. First, we present Zeus, an open-source testbed that emulates 5G channels to evaluate congestion control algorithms in a repeatable and reproducible manner. Second, we assess and characterize ten of today's state-of-the-art congestion control algorithms under different 5G environments and show the difficulties of these solutions to achieve high performance under highly varying 5G channel conditions. In addition, we also utilize the recently proposed harm metric to characterize the detriment done by such algorithms to TCP Cubic cross traffic in 5G environments.
... where η is in the range of (0, 1) and describes the efficiency of the transceiver design. W mmW ave i is the transmission bandwidth of the flow i, and N 0 is the onesided power spectra density of white Gaussian noise [26]. ...
With the explosive growth of mobile traffic demand, densely deployed small cells underlying macrocells have great potential for 5G and beyond wireless networks. In this paper, we consider the problem of supporting traffic flows with diverse QoS requirements by exploiting three high frequency bands, i.e., the 28 GHz band, the E-band, and the Terahertz (THz) band. The cooperation of the three bands is helpful for maximizing the number of flows with their QoS requirements satisfied. To solve the formulated nonlinear integer programming problem, we propose a triple-band scheduling scheme which can select the optimum scheduling band for each flow among three different frequency bands. The proposed scheme also efficiently utilizes the resource to schedule flow transmissions in time slots. Extensive simulations demonstrate the superior performance of the proposed scheme over three baseline schemes with respect to the number of completed flows and the system throughput.
