Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

Li, Jing; Niu, Yong; Wu, Hao; Ai, Bo; Chen, Sheng; Feng, Zhiyong; Zhang, Zhong; Wang, Ning

doi:10.1109/comst.2022.3176802

Cited by 24 publications

(5 citation statements)

References 264 publications

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“…Assuming that all BSs are equipped with directional antennas to support beam tracking, while UEs are equipped with omnidirectional antennas. Thus the antenna gain model can be expressed as Equation (2):…”

Section: Propagation Modelmentioning

confidence: 99%

“…By utilizing frequency bands within the range of 30 to 300 GHz, mmWave networks offer significant advantages over traditional networks operating below 6 GHz, including greater data rates exceeding 10 Gbps and access to abundant spectrum resources. These characteristics make mmWave networks highly promising for meeting the escalating capacity demands of future wireless communication systems 1,2 . Consequently, numerous mmWave communication technologies have been developed to deliver ultra‐high information rates for diverse services and applications 3–5 …”

Section: Introductionmentioning

confidence: 99%

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Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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Millimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

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Section: Propagation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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“…According to the theory of the Poisson point process (PPP), the authors in Reference 7 have used stochastic geometry to provide the analytical framework. By realistic channel models for mmWave propagation, it has been shown that dense mmWave cellular networks perform better than microwave cellular networks 8 . The work in Reference 9 has considered the 5G path loss models and evaluated the performance.…”

Section: Introductionmentioning

confidence: 99%

“…By realistic channel models for mmWave propagation, it has been shown that dense mmWave cellular networks perform better than microwave cellular networks. 8 The work in Reference 9 has considered the 5G path loss models and evaluated the performance.…”

mentioning

confidence: 99%

Beam tracking in phased array antenna based on the trajectory classification

Gilan

Paranjape

2023

Trans Emerging Tel Tech

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One of the major new concepts in 5G cellular communications is a shift in the way services are delivered to users. In the new 5G protocol, individual users are recognized and tracked through beams, which are targeted and specific to individual users. The millimeter-wave (mmWave) communications impose a directionality which results in a significant challenge in serving mobile terminals and unmanned aerial vehicles. This challenge can be relieved in mmWave systems using analog beamforming. Based on the identification of the patterns in the reference signal received power (RSRP) measurements, some classifications are employed. Some trajectories are defined for different users in Hallways. Therefore, the angle of arrival (AoA) would be the same for the users following the same trajectory. The users based on the received RSRP values are clustered by K-means and I-Kmeans algorithms. To this end, our algorithms to find the beamforming coefficient are employed for only one user in each cluster and the complexity of the algorithms can be lower. The proposed algorithms are spatial frequency-based beam tracking and angular domain beam tracking algorithms. Simulation results show that tracking the sine function of AoA achieves better performance compared to tracking the AoA. Moreover, the optimal number of clusters is obtained by using the Elbow method. INTRODUCTIONThe effects of 5G are visible on the Internet of Things (IoT), Augmented Reality (AR), unmanned aerial vehicles (UAV), industrial automation (IA), smart homes, smart cities, time-critical applications in telemedicine and telesurgery and so forth. 1 5G includes some different areas such as enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine type communications (mMTC). 2,3 Due to the direct impact on the public, this article focuses on the human-centric enhanced mobile broadband activity. 4 When new applications emerge, new requirements for greater broadband data are required and the demand for faster data is a growing option. 5 This article investigates the analysis of 5G millimeter-wave (mmWave) services in hotspots and for indoor-area coverage which will result in high user density and high capacity.Compared to sub-6-GHz communication links, the path loss at mmWave frequencies is greater. To alleviate this issue, phased-array techniques alongside Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) communication with an array antenna gain can be employed. In Reference 6, the performance of the coordinated multi-point (CoMP) based mmWave communication is analyzed. The aforementioned work considers the effect of the LoS component which is essential in mmWave communications due to the high path-loss properties of high frequency mmWave channels. Moreover, a

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“…For example, the propagation loss in free space at 60 GHz is 28 dB higher than in the 2.4 GHz band [4]. Therefore, in the mm-wave band, users need more transmit power to ensure the reliability of transmission, and in addition, to combat fading, the mm-wave band usually requires Line-of-Sight (LoS) communication [5].…”

mentioning

confidence: 99%

Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

Niu

Han

et al. 2023

IEEE Trans. Veh. Technol.

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In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RISassisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature.

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Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

Cited by 24 publications

References 264 publications

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Beam tracking in phased array antenna based on the trajectory classification

Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

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