Showing papers on "Data transmission published in 2019"

CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks

Abstract: This paper presents an energy-efficient static random access memory (SRAM) with embedded dot-product computation capability, for binary-weight convolutional neural networks. A 10T bit-cell-based SRAM array is used to store the 1-b filter weights. The array implements dot-product as a weighted average of the bitline voltages, which are proportional to the digital input values. Local integrating analog-to-digital converters compute the digital convolution outputs, corresponding to each filter. We have successfully demonstrated functionality (>98% accuracy) with the 10 000 test images in the MNIST hand-written digit recognition data set, using 6-b inputs/outputs. Compared to conventional full-digital implementations using small bitwidths, we achieve similar or better energy efficiency, by reducing data transfer, due to the highly parallel in-memory analog computations.

Deep Joint Source-channel Coding for Wireless Image Transmission

Abstract: We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect,” and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.

15.73 Gb/s Visible Light Communication With Off-the-Shelf LEDs

Abstract: Visible light communication (VLC) can provide high-speed data transmission that could alleviate the pressure on the conventional radio frequency spectrum with the looming capacity crunch for digital communication systems In this paper, we present experimental results of a VLC system with a data rate of 1573 Gb/s after applying forward error correction coding over a 16 m link Wavelength division multiplexing is utilized to efficiently modulate four wavelengths in the visible light spectrum Four single color low-cost commercially available light emitting diodes (LEDs) are chosen as light sources This confirms the feasibility and readiness of VLC for high-data rate communication Orthogonal frequency division multiplexing (OFDM) with adaptive bit loading is used The system with the available components is characterized and its parameters, such as LED driving points and OFDM signal peak-to-peak scaling factor, are optimized To the best of our knowledge, this is the highest data rate ever reported for LED-based VLC systems

A Secure FaBric Blockchain-Based Data Transmission Technique for Industrial Internet-of-Things

Abstract: The previous blockchain data transmission techniques in industrial Internet of Things (IoT) have low security, high management cost of the trading center, and big difficulty in supervision. To address these issues, this paper proposes a secure FaBric blockchain-based data transmission technique for industrial IoT. This technique uses the blockchain-based dynamic secret sharing mechanism. A reliable trading center is realized using the power blockchain sharing model, which can also share power trading books. The power data consensus mechanism and dynamic linked storage are designed to realize the secure matching of the power data transmission. Experiments show that the optimized FaBric power data storage and transmission has high security and reliability. The proposed technique can improve the transmission rate and packet receiving rate by 12% and 13%, respectively. Moreover, the proposed technique has good superiority in sharing management and decentralization.

Optical orbital-angular-momentum-multiplexed data transmission under high scattering

Abstract: Multiplexing multiple orbital angular momentum (OAM) channels enables high-capacity optical communication. However, optical scattering from ambient microparticles in the atmosphere or mode coupling in optical fibers significantly decreases the orthogonality between OAM channels for demultiplexing and eventually increases crosstalk in communication. Here, we propose a novel scattering-matrix-assisted retrieval technique (SMART) to demultiplex OAM channels from highly scattered optical fields and achieve an experimental crosstalk of –13.8 dB in the parallel sorting of 24 OAM channels after passing through a scattering medium. The SMART is implemented in a self-built data transmission system that employs a digital micromirror device to encode OAM channels and realize reference-free calibration simultaneously, thereby enabling a high tolerance to misalignment. We successfully demonstrate high-fidelity transmission of both gray and color images under scattering conditions at an error rate of <0.08%. This technique might open the door to high-performance optical communication in turbulent environments.

An Algorithm for Security Enhancement in Image Transmission Using Steganography

Abstract: Data communication through the public communication channels is insecure due to advanced technology available for interception of third party users. Therefore, an efficient data hiding technology is vital for secure data transmission especially when the system is connected with internet services. Audio steganography is one of the promising solutions for where the information is hidden over audio file. In this paper, a novel method for the hiding of image information by converting into another format thereby reduces the computational complexity.

Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks

Abstract: The Internet of Things (IoT) platform has played a significant role in improving road transport safety and efficiency by ubiquitously connecting intelligent vehicles through wireless communications. Such an IoT paradigm however, brings in considerable strain on limited spectrum resources due to the need of continuous communication and monitoring. Cognitive radio (CR) is a potential approach to alleviate the spectrum scarcity problem through opportunistic exploitation of the underutilized spectrum. However, highly dynamic topology and time-varying spectrum states in CR-based vehicular networks introduce quite a few challenges to be addressed. Moreover, a variety of vehicular communication modes, such as vehicle-to-infrastructure and vehicle-to-vehicle, as well as data QoS requirements pose critical issues on efficient transmission scheduling. Based on this motivation, in this paper, we adopt a deep ${Q}$ -learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. Furthermore, we investigate the characteristics of communication modes and spectrum resources chosen by vehicles in different network states, and propose an efficient learning algorithm for obtaining the optimal scheduling strategies. Numerical results are presented to illustrate the performance of the proposed scheduling schemes.

Constellation Learning-Based Signal Detection for Ambient Backscatter Communication Systems

Abstract: Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.

A Low-Power Wide-Area Network Information Monitoring System by Combining NB-IoT and LoRa

Abstract: To meet the requirements of long range, a small amount of data transmission, low power, and low cost of the Internet of Things (IoT) in actual applications, a low-power wide-area network information monitoring approach based on NB-IoT and LoRa is proposed in this paper. This approach adopts a communication mode that contains a main node and multiple subnodes to adapt to the needs of large-scale information monitoring. Among them, the design of the main node utilizes NB-IoT communication technology, LoRa communication technology, and least recently used algorithm. The design of subnode utilizes LoRa communication technology, sensor technology, and optical-electric conversion technology. Finally, we design and implement a cloud service and computing system, using the Tencent Cloud server. The advantage of this approach is that the combination of NB-IoT and LoRa not only improves transmission distance but also reduces the operating costs of the WAN information monitoring system. In addition, using optical-electric conversion technology, the system can be self-powered. Combined with the principle of electric circuits, the power consumption of the system can also be reduced. Finally, by conducting a system test, LoRa communication distance experiment, NB-IoT communication, and other experiments, the communication distance in a complex environment is up to 1.6 km, the minimum working current is 2 mA, and the system communication packet loss rate is approximately 3%. The system runs stably and the collected data are accurate. The results show that the proposed approach can meet the requirements of data collection, transmission, storage, and calculation in a wide area.

A Survey of Underwater Magnetic Induction Communications: Fundamental Issues, Recent Advances, and Challenges

Abstract: Enabling underwater wireless communications to conveniently interconnect various underwater deployed devices for data transmission, information sharing, and networking is among the most crucial issues in marine information networks. Apart from the conventional acoustic, optical, and electromagnetic techniques, the magnetic induction (MI) communication, as a promising alternative, has drawn significant attentions recently due to its inherent advantages in predictable channel responses, negligible propagation delay, and competitive energy consumption. In this paper, we attempt to provide a comprehensive and in-depth survey of the existing research on underwater MI communications, classified as the four topics of channel modeling, reliability guarantee, range extension, and capacity enhancement, and present the state-of-the-art advances on each topic. Specifically, the approaches for channel modeling of underwater MI communications, including proposed channel models and involved basic theories, are first summarized. Then, the existing works on reliability guarantee are expounded following the evolution of antenna design from traditional single-directional to advanced multi-directional MI antennas. Furthermore, as to range extension, two typical categories of MI relaying techniques, i.e., the MI waveguide and the active relaying techniques, are reviewed in detail. In particular, the potential of a hybrid relay transmission scheme proposed by us through flexible combination of these two techniques in achieving both energy-efficient and long-range underwater transmission is also addressed in this part. Finally, the existing approaches on extending or reusing the available frequency bands to enhance the underwater MI channel capacity are elaborated. Challenges and open issues that need to be further investigated related to each of the four topics are also profoundly discussed and analyzed in each part.

A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

Abstract: The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

Multipath Cooperative Routing with Efficient Acknowledgement for LEO Satellite Networks

Abstract: Multipath routing can significantly improve the network throughput and end-to-end (e2e) delay. Network coding based multipath routing removes the complicated coordination among multiple paths so that it further enhances data transmission efficiency. Traditional network coding based multipath routing protocols, however, are inefficient for Low Earth Orbit (LEO) satellite networks with the long link delay and regular network topology . Considering these characteristics, in this paper, we first formulate the multipath cooperative routing problem, then propose a Network Coding based Multipath Cooperative Routing (NCMCR) protocol for LEO satellite networks to improve the throughput. We propose source-based and destination-based multipath cooperative routing algorithms, which deliver different parts of a data flow along multiple link-disjoint paths dynamically and cooperatively. Furthermore, we design an efficient No-Stop-Wait ACK mechanism for our NCMCR protocol to accelerate the data transmission, where a source node continuously sends subsequent batches before it receives ACK messages for the batches sent previously. Under the proposed acknowledgement mechanism, we theoretically analyze the number of coded packets that should be sent and the transmission times of each batch for successfully decoding a batch. NS2-based simulation results demonstrate that our NCMCR outperforms the most related protocols.

A Low-Latency Communication Scheme for Mobile Wireless Sensor Control Systems

Abstract: Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

Sum-rate Maximization for Intelligent Reflecting Surface Based Terahertz Communication Systems

Abstract: Terahertz (THz) communication system is envisioned as a promising alternative to support ultra-high speed data transmission for future indoor application scenarios. Due to the existence of the potential obstacles, the line-of-sight communication links for indoor THz communication are not reliable. In this paper, we thus investigate the utilization of intelligent reflecting surface (IRS) to enhance the reflecting transmission of the THz communication system. Specifically, an IRS consists of a large number of reflecting elements, and the phase-shift of each reflecting element is adjustable. Based on the principle of IRS, the propagation direction of THz signals can be changed via adjusting all the phase-shifts of IRS, and then we are able to improve the sum-rate performance by selecting the optimal values of the phase-shifts. Accordingly, we first propose a local search (LS) method, which can greatly decease the complexity compared with the traditional exhaustive search method. However, the LS method suffers a certain performance loss. To this end, we then propose a cross-entropy (CE) method that is feasible to promote the sum-rate compared with the LS method. Numerical results verify the above conclusions, and also show the merit of the IRS-enhanced THz communication system.

Information cache management and data transmission algorithm in opportunistic social networks

Abstract: In social networks, nodes should analyze communication area during data transmission and find suitable neighbors to perform effective data classification transmission. This is similar to finding certain transmission destinations during data transmission with mobile devices. However, cache space with node in social opportunistic networks is limited, and waiting for destination node could also cause end-to-end delay. To improve the transmission environment, this study established a node recognition method for assessment probability, to satisfy the priority adjustment for the high probability nodes of cache, and then cache space should be reconstructed. To avoid accidentally deleting cached data, the cache task of the node is shared through the neighbor node cooperation, and the effective data transmission is performed. Through experiments and the comparison of social network algorithms, the proposed scheme improves delivery ratio by 82% and reduces delay by 74% with the traditional algorithms on average.

A Hybrid Communication Scheme for Efficient and Low-Cost Deployment of Future Flying Ad-Hoc Network (FANET)

Abstract: In recent years, FANET-related research and development has doubled, due to the increased demands of unmanned aerial vehicles (UAVs) in both military and civilian operations. Equipped with more capabilities and unique characteristics, FANET is able to play a vital role in mission-critical applications. However, these distinctive features enforce a series of guidelines to be considered for its efficient deployment. Particularly, the use of FANET for on-time data communication services presents demanding challenges in terms of energy efficiency and quality of service (QoS). Proper use of communication architecture and wireless technology will assist to solve these challenges. Therefore, in this paper, we review different communication architectures, including the existing wireless technologies, in order to provide seamless wireless connectivity. Based on the discussions, we conclude that a multi-layer UAV ad-hoc network is the most suitable architecture for networking a group of heterogeneous UAVs, while Bluetooth 5 (802.15.1) is the most favored option because of its low-cost, low power consumption, and longer transmission range for FANET. However, 802.15.1 has the limitation of a lower data rate as compared to Wi-Fi (802.11). Therefore, we propose a hybrid wireless communication scheme so as to utilize the features of the high data transmission rate of 802.11 and the low-power consumption of 802.15.1. The proposed scheme significantly reduces communication cost and improves the network performance in terms of throughput and delay. Further, simulation results using the Optimized Network Engineering Tool (OPNET) further support the effectiveness of our proposed scheme.

Secure Cache-Aided Multi-Relay Networks in the Presence of Multiple Eavesdroppers

Abstract: In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios of non-colluding and colluding eavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

Autonomous Energy-Efficient Wireless Sensor Network Platform for Home/Office Automation

Abstract: Smart homes/offices based on wireless sensor networks (WSNs) can provide an assisted living and working environment to the users. In these applications, the distributed network nodes are made up of low-power low-cost high-energy-efficient electronic platforms equipped with sensors, microcontroller, radio, and antenna, able to periodically sense, receive, store, pre-process, and transmit ambient data to a remote host station. Conventional nodes are usually supplied by batteries, resulting in a significant limitation to the lifetime and to the maximum number of deployable devices. To meet the demand of the next Internet-of-Things (IoT) applications, requiring a vast plurality of interconnected wireless network nodes, this paper presents the design and implementation of a WSN platform whose nodes are energetically autonomous thanks to an embedded photovoltaic panel associated to a rechargeable battery and a power-efficient design with optimized power-management strategy. The implemented node is able to harvest indoor ambient light starting from 100 lux and, according to the available energy, adaptively sets the sensors acquisition and RF transmission rate. Moreover, it provides long-distance data transmission with air data rate from 1 to 500 kbps. The WSN node device is implemented on an $8.6\times 5.4$ cm2 flexible PCB, being therefore amenable to conform even to curved surfaces. Comparison with the commercial IoT nodes reveals a significant improvement in the state of the art.

Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

Abstract: In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.

1 Gbps free-space deep-ultraviolet communications based on III-nitride micro-LEDs emitting at 262 nm

Abstract: The low modulation bandwidth of deep-ultraviolet (UV) light sources is considered as the main reason limiting the data transmission rate of deep-UV communications. Here, we present high-bandwidth III-nitride micro-light-emitting diodes (μLEDs) emitting in the UV-C region and their applications in deep-UV communication systems. The fabricated UV-C μLEDs with 566 μm2 emission area produce an optical power of 196 μW at the 3400 A/cm2 current density. The measured 3 dB modulation bandwidth of these μLEDs initially increases linearly with the driving current density and then saturates as 438 MHz at a current density of 71 A/cm2, which is limited by the cutoff frequency of the commercial avalanche photodiode used for the measurement. A deep-UV communication system is further demonstrated. By using the UV-C μLED, up to 800 Mbps and 1.1 Gbps data transmission rates at bit error ratio of 3.8×10−3 are achieved assuming on-off keying and orthogonal frequency-division multiplexing modulation schemes, respectively.

A Unified Design of Massive Access for Cellular Internet of Things

Abstract: With the increasing development of the cellular Internet of Things (IoT), the upcoming fifth-generation wireless network is required to support massive access of sporadic traffic devices. In this context, we design a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission, and downlink data transmission for the cellular IoT, so as to realize massive access over limited radio spectrum. We analyze the performance of the proposed transmission protocol and derive closed-form expressions for the uplink and downlink achievable rates in terms of channel conditions and system parameters. Moreover, to improve the overall performance, we propose a length allocation algorithm by coordinating the three-phase transmission protocol in the unified sense. Extensive simulation results show that substantial performance gain can be obtained by the proposed algorithm.

Data Transmission Reduction Schemes in WSNs for Efficient IoT Systems

Abstract: Spatial and temporal correlation among the generated traffic in wireless sensor networks (WSNs) can be exploited in reducing the energy consumption of continuous sensor data collection. Dual prediction (DP) and data compression (DC) schemes rely on the spatio-temporal correlation to reduce the number of transmissions across WSNs, which leads to conserving energy and bandwidth. In this paper, we present both schemes in a two-tier data reduction framework. The DP scheme is used to reduce transmissions between cluster nodes and cluster heads, while the DC scheme is used to reduce traffic between cluster heads and sink nodes. For both schemes, various algorithms will be studied and compared in terms of accuracy, delay, and transmission reduction percentage. For the DP scheme, neural networks (NNs) and long short-term memory networks (LSTMs) are proposed to perform predictions. The training phase of the NNs and LSTMs is done online which is necessary in the DP scheme. The performance will be compared to popular least-mean-square approaches. Regarding the DC scheme, principal component analysis, non-negative matrix factorization, truncated-singular value decomposition, and discrete wavelet transform will be discussed and compared. This paper focuses on comparative analysis of various data reduction algorithms alongside the proposed ones. Finally, design challenges and open research areas for having more transmission reductions will be presented.

Performance enhancement for energy harvesting based two-way relay protocols in wireless ad-hoc networks with partial and full relay selection methods

Abstract: In this paper, we propose two relay selection methods to enhance system outage performance for energy harvesting (EH) based two-way relaying protocols in wireless adhoc networks. In the proposed protocol, two source nodes communicate with each other via the assistance of multiple decode-and-forward (DF) relays using three-phase digital network coding. At the first and second phases, two sources broadcast their data to the relays. Employing a power-splitting model, the relays would harvest energy from radio frequency (RF) signals of the sources to transmit the data at the third phase. We propose a simple partial relay selection (PRS) method and an opportunistic relay selection (ORS) method to enhance the reliability of data transmission at the cooperative phase. For performance evaluation, we derive exact and asymptotic expressions of the system outage probability (SOP) for the proposed protocols over block Rayleigh fading channels. Finally, Monte Carlo simulations are presented to verify the theoretical derivations as well as to compare the performance of the proposed protocols with that of the random relay selection protocol.

Parallel Data Transmission in Indoor Visible Light Communication Systems

Abstract: This paper presents an indoor visible light communication (VLC) system in conjunction with an imaging receiver with parallel data transmission (spatial multiplexing) to reduce the effects of the inter-symbol interference (ISI). To distinguish between light units (transmitters) and to match the light units used to convey the data with the pixels of the imaging receiver, we propose the use of subcarrier multiplexing (SCM) tones. Each light unit transmission is multiplexed with a unique tone. At the receiver, a SCM tone decision system is utilized to measure the power level of each SCM tone and consequently associate each pixel with a light unit. In addition, the level of co-channel interference (CCI) between light units is estimated using the SCM tones. Our proposed system is examined in two indoor environments taking into account reflective components (first and second order reflections). The results show that this system has the potential to achieve an aggregate data rate of 8 Gb/s with a bit error rate of 10–6 for each light unit, using simple on-off-keying (OOK).

Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

Abstract: In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.

Performance Analysis of Joint Radar and Communication using OFDM and OTFS

Abstract: We consider a joint radar estimation and communication system using orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) modulations. The scenario is motivated by vehicular applications where a vehicle equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. By focusing on the case of a single target, we derive the maximum likelihood (ML) estimator and the Cramér-Rao lower bound on joint velocity and range estimation. Numerical examples demonstrate that both digital modulation formats can achieve as accurate range/velocity estimation as state-of-the-art radar waveforms such as frequency modulated continuous wave (FMCW) while sending digital information at their full achievable rate. We conclude that it is possible to obtain significant data transmission rate without compromising the radar estimation capabilities of the system.

THz and Photonic Seamless Communications

Abstract: Terahertz (THz) wave can offer over 100-Gb/s wireless transmission by the use of wide spectrum. Due to features of high-frequency waves such as lightwaves, THz can have high directivity. To mitigate congestion of radio spectrum, advanced modulation formats will play significant roles in THz bands as well as in conventional millimeter-wave or microwave bands. This paper will provide overviews on spectral efficiency and transmission capacity of THz wave systems. Power consumption of radio links would be also an important issue to reduce OPEX of networks. Survey on power consumption of short-distance wireless systems implies that high-speed radio transmission using THz bands would provide low power consumption transmission. However, THz transmission distance will be limited in shorter than a few kilometers due to attenuation in the air. Seamless networks consisting of THz links and optical fibers would provide high-speed, low-latency, and low power consumption data transmission. Devices and signal processing functions developed for optical fiber transmission can be applied to high-speed THz communications.

Energy-Efficient Harvested-Aware Clustering and Cooperative Routing Protocol for WBAN (E-HARP)

Abstract: Wireless body area network (WBAN) is an interconnection of small bio-sensor nodes (BSNs) that are deployed in/on different parts of human body. It is used to sense health-related data, such as rate of heart beat, blood pressure, blood glucose level, electro-cardiogram (ECG), and electro-myography (EMG), of human body and pass these readings to real-time health monitoring systems. WBANs are the important research area and are used in different applications, such as medical field, sports, entertainment, and social welfare. BSNs or simply called sensor nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of the WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic cluster head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated cost factor (CF). The parameters used for calculation of CF are residual energy of SN, required transmission power, communication link signal-to-noise ratio (SNR), and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data are routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using the NS-2 simulation tool has been conducted. The results are compared with some latest techniques, named EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay, and packet delivery ratio.

Data prediction model in wireless sensor networks based on bidirectional LSTM

Abstract: The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.