In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

UAV-Aided Backscatter Communications: Performance Analysis and Trajectory Optimization

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input&#x2013;multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time. 

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A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.

Joint Communication and Trajectory Optimization for Multi-UAV Enabled Mobile Internet of Vehicles

5G in manufacturing: a literature review and future research

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

Deep Learning-Based Cellular Random Access Framework

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space-air-ground integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in the IoV, where aggregated packets can be relayed to a base station (BS). In this paper, we first propose an aggregators-aided random access (ARA) scheme for the IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a Semi-Markov chain is used to analyze the average number of aggregated packets and the metric of average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize the ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Data aggregation in UAV-aided random access for the Internet of Vehicles

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

A Collision Resolution Protocol for Random Access in Massive MIMO

To extend the conventional wireless sensor networks (WSNs) to support wider applications such as intruder detection and border security monitoring, active radar sensors are introduced into WSNs to further enhance their capability, thus forming wireless radar sensor networks (WRSNs). To improve the network efficiency, the technology of integrated sensing and communication (ISAC) can be applied to co-design the sensing and communication functionalities of radar sensors. Since the cooperative operations of WRSNs require effective information interaction among radar sensors, data dissemination techniques need to be investigated, which become even more critical in desolate areas without the coverage of the base stations (BSs). Therefore, in this paper, a duty cycling mechanism is applied to the network to enhance the usage of WRSNs and support data dissemination, where a storage node is deployed to store the data spreading from radar sensors and a mobile data collector is employed to collect the data from the storage node periodically. Then, the epidemic theory, as an innovative tool for modeling data dissemination, is adopted to analyze the performance of WRSNs. After epidemiological modeling, the density of radar sensors is optimized by the epidemiological analytical method to maximize the throughput of the storage node by jointly considering the functions of radar detection and communication. Simulation results validate the accuracy of analysis, which also show the efficiency of the optimization for data dissemination.

Wireless Radar Sensor Networks: Epidemiological Modeling and Optimization

The integrated sensing and communication (ISAC) has been proposed as an enabling technology for improving the network efficiency, where the wireless infrastructures and spectrum resources can be reused by co-designing the sensing and communication (S&C) functionalities. Different networks adopt the technique of ISAC to achieve the enhanced capabilities in precise localization, tracking, and imaging. To further improve the performance of cooperative detection, data dissemination can be carried out for ISAC-enabled networks. However, as sensing information sharing among S&C devices generally proceeds in a multi-hop manner, there exist various design challenges in ISAC-enabled networks, e.g., data dissemination modeling and deployment of S&C devices. Since epidemic theory has been developed as an innovative tool for modeling data dissemination, it can also be applied to design and analyze the ISAC-enabled network. In this article, we first introduce the ISAC-enabled network as well as several design challenges. Then, we present some key design issues of the ISAC-enabled network, including the sensing information dissemination, where some scenarios or use cases for practical applications of the ISAC-enabled network are given to validate the effectiveness of the epidemic theory-based modeling. Finally, the potential directions of epidemic theory on ISAC-enabled networks are discussed to motivate future works.

Epidemic Theory-Inspired Integrated Sensing and Communication Networks: Design and Analysis

In a decentralized organization such as a university, recruitment is critical for the development of departments. With the limited resources for recruitment, how to allocate the recruitment quotas to different departments is an essential problem for the human resource management center of the organization. Specifically, for a recruitment process that includes multiple phases, a proper number of quotas should be allocated to different departments at each recruitment phase to guarantee the recruitment performance of the whole organization. However, it is difficult since the performance of the whole recruitment process is prior unknown at each recruitment phase. Traditionally, a recruitment quota allocation scheme that evenly allocates recruitment quotas at different phases of a recruitment process is generally adopted, which is somewhat subjective due to the lack of quantitative analysis and may not achieve satisfying recruitment performance. Therefore, in this paper, a recruitment quota allocation scheme is proposed to provide a more objective and theoretical method that may achieve optimal performance. In the proposed scheme, we evaluate its performance by considering the competence of applicants and the number of potential applicants and obtain an improved recruitment quota allocation policy using the Markov decision process approach. Specifically, we first define the recruitment utility as the performance metric for the recruitment process. The recruitment quota allocation problem is formulated using the Markov decision process approach, where the effect of the information of potential applicants is innovatively taken into account. Then, the recruitment quota allocation is optimized by maximizing the recruitment utility to achieve optimal recruitment performance. In addition, a scheme that evenly allocates recruitment quotas at different phases of a recruitment process, which is traditionally adopted in the recruitment of a university, is used for comparison with the optimized scheme of the paper. Simulation results show that it is better to appropriately allocate more recruitment quotas in the early phases of a recruitment process than to allocate the quotas evenly over phases. Besides, it is also shown that the proposed scheme can improve the efficiency of recruitment quota allocation significantly.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09992214.pdf

Jiexun Liu

Papers

Data aggregation in UAV-aided random access for the Internet of Vehicles

A Collision Resolution Protocol for Random Access in Massive MIMO

Wireless Radar Sensor Networks: Epidemiological Modeling and Optimization

Epidemic Theory-Inspired Integrated Sensing and Communication Networks: Design and Analysis

Optimization of the Recruitment Quota Allocation in Intra-Organizational Networks