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High communication speed and sufficient energy supply are the directions of technological development. Energy and information available anywhere and anytime has always been peoples good wishes. On this basis, resonant beam system (RBS) has demonstrated its unique superiority in meeting the needs for energy and communication. The previous work has mostly focused on the analysis of charging performance of RBS and its steady-state characteristics. In order to analyze the communication performance of RBS more thoroughly, we propose a resonant beam charging and communication (RBCC) system and use the equivalent circuit analysis method to conduct transient analysis on it. The equivalent circuit reveals the dynamic establishment process of the resonant beam from scratch, which facilitates the analysis of the relaxation oscillation process and a deeper understanding of the energy transmission and communication performance. In addition, we explore the energy transmission and communication performance of the RBCC under different energy allocation strategies.
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Elastic optical network (EON) efficiently utilize spectral resources for optical fiber communication by allocating the minimum necessary bandwidth to client demands. On the other hand, network traffic has been continuously increasing due to the wide penetration of video streaming services, so the efficient and cost-effective use of available bandwidth plays an important role in improving service provisioning. In this work, we formulate and solve an optimization problem to perform routing and spectrum assignment (RSA) in EON with focus on video streaming. In this formulation, EON and video constraints such as spectrum fragmentation and received video quality are considered jointly. In this way, we utilize a machine learning (ML) technique to estimate the video quality versus channel state. The proposed algorithm is evaluated over two benchmarks fiber-optic network, namely NSFNET and US-backbone using numerical simulations based on random traffic models. The results reveal that the mean optical signal-to-noise ratio (OSNR) for video content data in the receiver is remarkably higher than in non-video data. This is while the blocking ratio is the same for both data types.
Considering the energy-efficient emergency response, subject to a given set of constraints on emergency communication networks (ECN), this article proposes a hybrid device-to-device (D2D) and device-to-vehicle (D2V) network for collecting and transmitting emergency information. First, we establish the D2D network from the perspective of complex networks by jointly determining the optimal network partition (ONP) and the temporary data caching centers (TDCC), and thus emergency data can be forwarded and cached in TDCCs. Second, based on the distribution of TDCCs, the D2V network is established by unmanned aerial vehicles (UAV)-based waypoint and motion planning, which saves the time for wireless transmission and aerial moving. Finally, the amount of time for emergency response and the total energy consumption are simultaneously minimized by a multiobjective evolutionary algorithm based on decomposition (MOEA/D), subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR), number of UAVs, transmit power, and energy constraints. Simulation results show that the proposed method significantly improves response efficiency and reasonably controls the energy, thus overcoming limitations of existing ECNs. Therefore, this network effectively solves the key problem in the rescue system and makes great contributions to post-disaster decision-making.
The society as a whole, and many vertical sectors in particular, is becoming increasingly digitalized. Machine Type Communication (MTC), encompassing its massive and critical aspects, and ubiquitous wireless connectivity are among the main enablers of such digitization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society. However, it is evident that some of the more demanding requirements cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and discusses the following six key research questions: - Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will there emerge new key metrics? - How to deliver different E2E service mandates with different KPI requirements considering joint-optimization at the physical up to the application layer? - What are the key enablers towards designing ultra-low power receivers and highly efficient sleep modes? - How to tackle a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy for global MTC connectivity? - How to support new service classes characterizing mission-critical and dependable MTC in 6G? - What are the potential enablers of long term, lightweight and flexible privacy and security schemes considering MTC device requirements?
Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication.
Future wireless networks are envisioned to serve massive Internet of things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, the theoretical analysis of the RACH procedure for massive IoT devices is challenging. To address this challenge, we first correlate the RACH request of an IoT device with the status of its maintained queue and analyze the evolution of the queue status. Based on the analysis result, we then derive the closed-form expression of the random access (RA) success probability, which is a significant indicator characterizing the RACH procedure of the device. Besides, considering the agreement on converging different services onto a shared infrastructure, we investigate the RAN slicing for mIoT and bursty ultra-reliable and low latency communications (URLLC) service multiplexing. Specifically, we formulate the RAN slicing problem as an optimization one to maximize the total RA success probabilities of all IoT devices and provide URLLC services for URLLC devices in an energy-efficient way. A slice resource optimization (SRO) algorithm exploiting relaxation and approximation with provable tightness and error bound is then proposed to mitigate the optimization problem. Simulation results demonstrate that the proposed SRO algorithm can effectively implement the service multiplexing of mIoT and bursty URLLC traffic.
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