With small cell networks becoming core parts of the fifth generation (5G) cellular networks, it is an important problem to evaluate the impact of user mobility on 5G small cell networks. However, the ...tendency and clustering habits in human activities have not been considered in traditional user mobility models. In this paper, human tendency and clustering behaviors are first considered to evaluate the user mobility performance for 5G small cell networks based on individual mobility model (IMM). As key contributions, user pause probability, user arrival, and departure probabilities are derived in this paper for evaluating the user mobility performance in a hotspot-type 5G small cell network. Furthermore, coverage probabilities of small cell and macro cell BSs are derived for all users in 5G small cell networks, respectively. Compared with the traditional random waypoint (RWP) model, IMM provides a different viewpoint to investigate the impact of human tendency and clustering behaviors on the performance of 5G small cell networks.
Intelligent connected vehicles have garnered significant attention from both academia and industry in recent years as they form the backbone of intelligent transportation and smart cities. Vehicular ...networks now exchange a range of mixed information types, including safety, sensing, and multimedia, due to advancements in communication and vehicle technology. Accordingly, performance requirements have also evolved, prioritizing higher spectral efficiencies while maintaining low latency and high communication reliability. To address the trade-off between communication spectral efficiency, delay, and reliability, the 3rd Generation Partnership Project (3GPP) recommends the 5G NR FR2 frequency band (24 GHz to 71 GHz) for vehicle-to-everything communications (V2X) in the Release 17 standard. However, wireless transmissions at such high frequencies pose challenges such as high path loss, signal processing complexity, long pre-access phase, unstable network structure, and fluctuating channel conditions. To overcome these issues, this paper proposes a deep reinforcement learning (DRL)-assisted intelligent beam management method for vehicle-to-vehicle (V2V) communication. By utilizing DRL, the optimal control of beam management (i.e., beam alignment and tracking) is achieved, enabling a trade-off among spectral efficiency, delay, and reliability in complex and fluctuating communication scenarios at the 5G NR FR2 band. Simulation results demonstrate the superiority of our method over the 5G standard-based beam management method in communication delay, and the extended Kalman Filter (EKF)-based beam management method in reliability and spectral efficiency.
As an essential part of vehicle networks, the Vehicle to Infrastructure (V2I) needs the support of millimeter wave and massive MIMO technologies to enable high data rate applications, such as ...automated driving, real-time high-quality multimedia services and so on. As the scale of the antenna array increases, the complexity of the beamforming and channel estimation algorithms under high mobility conditions also increases significantly. In particular, highly robust beamforming methods need to cope with fast changing transmission environments. In this paper, we adopt a biological inspired self-adaptive selection algorithm called attractor selection algorithm (ASA) to support uplink beamforming. The ASA requires only a little feedback information from the Road Side Infrastructure (RSI) to perform fast beam training, hence making the transmission link more stable. The simulation results indicate that the proposed ASA-assisted algorithm can significantly reduce the time required to achieve a timely beam training, which would be essential for V2I high communications under high mobility conditions.
It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi ...tessellation (PVT) random cellular networks. To evaluate the user access to the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.
Beam tracking is a core issue in 5G vehicle-to-everything (V2X) networks. Specifically, higher beamforming gain is required to compensate for the path loss at higher frequencies, e.g., 5G FR2, to ...realize high data rate vehicle-to-infrastructure (V2I) communications. However, shorter time slots at higher frequencies, high velocity of vehicles, and unpredictable localization errors make this problem more challenging. Under these circumstances, wider beams can lead to higher beam tracking accuracy. Bear in mind that wider beams mean lower beamforming gain, which cannot compensate for high path loss at high frequencies and would further influence the data rate of V2I communications. Thus, there exists a trade-off between tracking accuracy and data rate in V2I communications. Furthermore, this problem needs to be solved within an extremely short time slot according to the high transmission frequency. To solve this problem, we propose a reinforcement learning (RL) assisted, high-resolution codebook-based beam tracking method. By comparing several different RL frameworks, we found that the twin delayed deep deterministic policy gradient (TD3) framework can help the roadside infrastructure (RSI) determine a proper beam pattern within a short duration. Moreover, according to the Hurst exponent analysis, recurrent neural networks (RNNs) are selected to improve the performance of the RL framework. The simulation results show that the proposed method performs well in tracking accuracy, data rate, and temporal efficiency.
As a fundamental catalyst for intelligent transportation systems, the utilization of beyond-fifth-generation (B5G) technology in vehicle-to-everything (V2X) communication has garnered global ...attention. To tackle the performance challenge, the 3rd generation partnership project (3GPP) has stipulated in its Release 17 standard that the 5G new radio (NR) frequency range 2 (FR2, from 24 GHz to 71 GHz) can be employed for V2X communication. However, V2X communication, especially vehicle-to-Infrastructure (V2I) communication, is affected by the Doppler shift and delay spread, which becomes more pronounced as the transmission frequency rises. This presents additional obstacles for vehicle communication in the FR2. In this article, we propose a deep reinforcement learning (DRL)-assisted hybrid precoding method for V2I communication on the 5G NR FR2 band, aming to strike a balance between complexity, reliability, and data rate, while considering the impact of strong Doppler shift and delay spread. Specifically, we present a downlink transmission model using massive multi-input multi-output (mMIMO) and hybrid precoding to assess the influence of these variables, leading to the evaluation of mismatch probability. Subsequently, a DRL-assisted hybrid precoding method is introduced, leveraging a multi-level codebook to optimize spectral efficiency without excessive complexity. Through rigorous data analysis, we highlight the benefit of incorporating RNNs within the DRL framework, enhancing training efficiency. A comparative study reveals the twin delayed deep deterministic policy gradient (TD3) model coupled with RNN outperforms other methods in terms of spectral performance.
Sub-THz-based communication needs ultra-large antenna arrays to generate pencil-like beams under the stringent requirements of line-of-sight (LoS) to overcome the effect of the rapid attenuation of ...signal strength, including atmospheric attenuation. In addition, the complexity of attaining narrower beamwidth, which also requires fast and perfect alignment between the TX-RX pair, makes the sector sweeping-based beam discovery approach no longer suitable. To solve these challenging problems, in this paper we propose a leaky waveguide (LWG)-assisted, high-resolution codebook-based beam discovery, precoding, and combining scheme together with an adaptive frequency hopping spread spectrum (FHSS) for a network with relays and small base stations (SBS). The proposed method can help the transmitter quickly determine a suitable beam pattern for an LoS path requiring only limited feedback information. In the beam discovery phase, we use LWG to attain valuable information about the angle of departure (AoD) and angle of arrival (AoA). LWG has also been used to identify frequency selective attenuated regions, which can change from time to time depending on the local atmospheric moisture conditions. To mitigate the impact of such attenuations, we opt for an adaptive Frequency Hopping Spread Spectrum (FHSS). The simulation results indicate that our beam discovery and FHSS based method can achieve a much higher performance than baseline methods.
Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a promising approach to deal with the ever-growing traffic that can severely impact the power consumption of ...small-cell networks. Unfortunately, the amount of harvested energy, which depends on the transmission environment, is highly random and difficult to predict. Since there may be multiple sources of energy in the HUDN, e.g., macro base stations or TV towers, the challenging issue is when and where to harvest energy. Optimally controlling the HUDN can profoundly influence the performance of both data transmission and energy harvesting. However, the working pattern of individual small cell base stations needs to be determined in every time slot. To find an optimal solution in a highly random environment we propose reinforcement learning methods, such as deep deterministic policy gradient (DDPG) and wolpertinger DDPG (W-DDPG). Since the action space is large and discrete for the controlling tasks, a W-DDPG algorithm has been found to be the best approach. The simulation results verify that, compared with the original DDPG algorithm and deep Q-learning, the proposed W-DDPG method can achieve a superior performance in terms of both energy efficiency and throughput.
User distribution in ultradense networks (UDNs) plays a crucial role in affecting the performance of UDNs due to the essential coupling between the traffic and the service provided by the networks. ...Existing studies are mostly based on the assumption that users are uniformly distributed in space. The nonuniform user distribution has not been widely considered despite that it is much closer to the real scenario. In this paper, radiation and absorbing model (R&A model) is first adopted to analyze the impact of the nonuniformly distributed users on the performance of 5G UDNs. Based on the R&A model and queueing network theory, the stationary user density in each hot area is investigated. Furthermore, the coverage probability, network throughput, and energy efficiency are derived based on the proposed theoretical model. Compared with the uniformly distributed assumption, it is shown that nonuniform user distribution has a significant impact on the performance of UDNs.
The fifth-generation (5G) based vehicle-to-everything (V2X) communication is a promising technology to enhance both manned and unmanned driving systems. To this end, the 5G V2X networks should ...satisfy stringent requirements on transmission reliability and delay for exchanging safety-critical messages (SCMs). A joint analysis of transmission reliability and delay in V2X communication networks is thus crucial, particularly in urban 5G V2X networks. This was considered prohibitive due to the complicated spatial-temporal dynamics of V2X communications caused by interference, channel fading, as well as queueing and retransmission of SCMs. Moreover, urban 5G V2X networks are typically deployed in a finite area, where locations of nodes are spatially correlated and cannot be conveniently modeled as Poisson point process (PPP). In this paper, we propose a novel binomial point process (BPP) based analytic framework for modeling the spatial-temporal dynamics of urban 5G V2X communications and characterizing transmission reliability and delay of SCM exchange jointly. The presented framework captures not only the spatial distribution of interference and channel fading during uplink and downlink transmissions, but also the temporal dynamics associated with queueing and retransmissions of SCMs. Exploiting the stochastic geometry theory and queueing theory, closed-form expressions of transmission reliability and delay are derived, which are further validated using Monte Carlo simulations. Both the numerical and simulation results reveal complicated couplings between the transmission reliability and delay in different operation regimes. Nevertheless, the proposed analytical framework can accurately capture the reliability-delay relations.
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