On the Optimum Energy Efficiency for Flat-fading Channels with Rate-dependent Circuit Power

This paper investigates the optimum energy efficiency (EE) and the corresponding spectral efficiency (SE) for a communication link operating over a flat-fading channel. The EE is evaluated by the total energy consumption for transmitting per message bit. Three channel cases are considered, namely static channel with channel state information available at transmitter (CSIT), fast-varying (FV) channel with channel distribution information available at transmitter (CDIT), and FV channel with CSIT. A general circuit power model is considered. For all the three channel cases, the tradeoff between the EE and SE is studied. It is shown that the EE improves strictly as the SE increases from 0 to the optimum SE, and then strictly degrades as the SE increases beyond the optimum SE. The impact of {\kappa}, {\rho} and other system parameters on the optimum EE and corresponding SE is investigated to obtain insight.Some of the important and interesting results for all the channel cases include: (1) when {\kappa} increases the SE corresponding to the optimum EE should keep unchanged if {\phi}(R) = R, but reduced if {\phi}(R) is strictly convex of R; (2) when the rate-independent circuit power {\rho} increases, the SE corresponding to the optimum EE has to be increased. A polynomial-complexity algorithm is developed with the bisection method to find the optimum SE. The insight is corroborated and the optimum EE for the three cases are compared by simulation results.Comment: 12 pages, 7 figures, to appear in IEEE Transactions on Communication

Sum-Rate and Power Scaling of Massive MIMO Systems with Channel Aging

2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively.Peer reviewe

LTE-Advanced radio access enhancements: A survey

Long Term Evolution Advanced (LTE-Advanced) is the next step in LTE evolution and allows operators to improve network performance and service capabilities through smooth deployment of new techniques and technologies. LTE-Advanced uses some new features on top of the existing LTE standards to provide better user experience and higher throughputs. Some of the most significant features introduced in LTE-Advanced are carrier aggregation, enhancements in heterogeneous networks, coordinated multipoint transmission and reception, enhanced multiple input multiple output usage and deployment of relay nodes in the radio network. Mentioned features are mainly aimed to enhance the radio access part of the cellular networks. This survey article presents an overview of the key radio access features and functionalities of the LTE-Advanced radio access network, supported by the simulation results. We also provide a detailed review of the literature together with a very rich list of the references for each of the features. An LTE-Advanced roadmap and the latest updates and trends in LTE markets are also presented

Programmable Metasurface Based Multicast Systems: Design and Analysis

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements

Smart handoff technique for internet of vehicles communication using dynamic edge-backup node

© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/electronics9030524A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.Published onlin

Cyber-physical systems in food production chain

The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products).The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products)

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)