Two-way Communication Link Research Articles

Designing and Testing an IoT Low-Cost PPP-RTK Augmented GNSS Location Device.

Nowadays, the availability of affordable multi-constellation multi-frequency receivers has broadened access to accurate positioning. The abundance of satellite signals coupled with the implementation of ground- and satellite-based correction services has unlocked the potential for achieving real-time centimetre-level positioning with low-cost instrumentation. Most of the current and future applications cannot exploit well-consolidated satellite positioning techniques such as Network Real Time Kinematic (RTK) and Precise Point Positioning (PPP); the former is inapplicable for large user bases due to the necessity of a two-way communication link between the user and the NRTK service provider, while the latter necessitates long convergence times that are not in keeping with kinematic application. In this context, the hybrid PPP-RTK technique has emerged as a potential solution to meet the demand for real-time, low-cost, accurate, and precise positioning. This paper presents an Internet of Things (IoT) GNSS device developed with low-cost hardware; it leverages a commercial PPP-RTK correction service which delivers corrections via IP. The main target is to obtain both horizontal and vertical decimetre-level accuracies in urban kinematic tests, along with other requisites such as solution availability and the provision of connection ports for interfacing an IoT network. A vehicle-borne kinematic test has been conducted to evaluate the device performance. The results show that (i) the IoT device can deliver horizontal and vertical positioning solutions at decimetre-level accuracy with the targeted solution availability, and (ii) the provided IoT ports are feasible for gathering the position solutions over an internet connection.

A method for maintaining a stable ISM band point-to-point wireless communication link in the presence of interference

This paper presents a new method that enables automatic channel change in case of intense interference on the active channel of a wireless communication point-to-point link. Channel change algorithms on the master and slave side of the communication link are presented, which enable the automatic selection of a new communication channel from a predefined set of available communication channels. By applying this method, it is possible to maintain a stable communication link in case of intense unpredictable interference on the transmitter or receiver side. The method applies to two-way master-slave communication links, regardless of the frequency spectrum and the number of used communication channels.

Power Optimization of a Three-Node Two-Way Relay-Assisted Power-Line Communication System

A smart grid system requires two-way communication links. Because power-line infrastructure is widely available, power-line communication (PLC) is preferred as the communication technology in smart grid systems. A power-line, on the other hand, was not designed to carry data and thus suffers from disturbances that attenuate the transmitted signal. Therefore, it is beneficial to provide spatial diversity to improve the system performance. Optimal power allocation has always been an issue to be solved in relay-aided communication systems. Using a two-way multicarrier relay configuration, this paper investigates power optimization of a relay-assisted PLC system with the consideration of the quality of service (QoS) constraints. Further, we investigate the combination of half-duplex and full-duplex nodes. Since the QoS constrained power allocation problem is highly non-convex, an alternating optimization (AO) algorithm is proposed to decompose the optimization problem into sub-problems. The AO algorithm is an iterative algorithm where it uses the newly obtained parameters (e.g., power) to find a new set of other parameters. Simulation results show that the proposed system successfully reduces the total power required by the system compared to the conventional bidirectional direct transmission (BDT) system and the relay-assisted two-way information exchange (R2WX) system under the same QoS requirement.

Real-Time Machine Learning Methods for Two-Way End-to-End Wireless Communication Systems

In this article, we study a data-driven real-time machine learning method for end-to-end wireless systems for the Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the autoencoder-based transmitter and receiver in each device without needing to know the channel between two devices. To this end, we adopt the conditional generative adversarial network (cGAN) that can learn an output distribution of the channel for a given conditioning signal. Our proposed training method consists of the link update stage and the self-update stage. In the link update stage, two devices transmit the training data and update their own receiver and the cGAN simultaneously. Subsequently, in the self-update stage, the two devices train their transmitters at the same time for the given receivers and cGANs. Our proposed real-time training method is updated without the knowledge of the channel models nor information feedback for training. Finally, we demonstrate that the proposed training method achieves significant performance gains over conventional schemes in various practical communication scenarios.

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

An intellectual procurement innovation of smart grid power system with wireless communication networks based on machine learning

The phased array antenna is one of the most significant applications in fifth-generation mobile networks. It is one of the most important applications in fifth-generation networks. An electric power source that powers the whole application, including the antenna's root, is required. Even with the most outstanding design, if the programme does not have a sound power supply system with minimal packet loss and can't Path find performance, it will be rendered ineffective. When seen from the perspective of the multiplex information, Machine Learning comprises a communication network based on the Internet that transmits information to the control centre via the objects (IOT). To put it another way, the proposed communication infrastructure, via the provision of, and the chance micro-grid state to collect, analyze, and two-way communication link control information, offers the chance to resolve the voltage regulation issues. This cutting-edge communication infrastructure, as well as a suggested state estimation filter focused on improving speed and performance in renewable energy production, are both examples of creative communication infrastructure. Current research is focused on analyzing and enumerating a range of energy abundances in the context of smart grids, which are now in their fifth generation. Rather than concentrating on the future development plan, which should be a problem of illusion, it should concentrate on the composition of the future potential of smart grid communications framework. An in-depth investigation to give evidence to the Machine learning will help intelligent networks in the future conduct a thorough evaluation.

Multiple blanking preprocessors for impulsive noise mitigation in OFDM-based power-line communication systems

Power-line communication (PLC) technology has been a critical enabler for smart grid systems. It provides two-way communication links without new infrastructure deployment. However, as the power-line was not designed to carry data, there are many drawbacks when it is used as a communication channel. One of the major drawbacks is impulsive noise occurrence. Blanking nonlinear preprocessor has been commonly used for mitigating impulsive noise in orthogonal frequency-division multiplexing (OFDM)-based PLC. It uses a threshold to detect whether a signal sample is contaminated by impulsive noise. Optimum performance is obtained when the optimal blanking threshold is used. The optimal blanking threshold depends on noise parameters, which are usually assumed to be available to the receiver. However, they may not be known in practice. In this paper, we propose a blanking preprocessor bank consisting of multiple blanking preprocessors with different thresholds which blindly handles the impulsive noise. Each blanking preprocessor is considered to provide an independent channel. The output of all preprocessors is combined using the maximal ratio combining (MRC) technique at the OFDM demodulator. Simulation results show that the proposed blanking preprocessor bank method results in better performance compared to the one using the optimal threshold method.

Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.

Optimum consultation for parallel distributed detection systems

An optimum consultation scheme for parallel distributed detection system is proposed. Two-way communication links are assumed between a set of local sensors and the fusion center. The observations of each sensor are assumed to be statistically independent of all other sensors observations. We also show that the proposed consulting scheme can be optimized such that its performance is identical to that of the optimum parallel scheme. It should be emphasized that the proposed consulting system exhibits the advantage of conditional nonrandom consultation between the fusion center and the sensors. Therefore, the sensors are less detectable which gives the system higher chance of survivability than that of the typical parallel system. Performance results for the case of two and the case of three sensors are given and compared with those of the centralized scheme.

BAP: A Batch and Auditable Privacy Preservation Scheme for Demand Response in Smart Grids

Advancing network technologies allow the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove their data consumption's adjustment by using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, data usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.

Sum-Throughput Maximization in Wireless Sensor Networks With Radio Frequency Energy Harvesting and Backscatter Communication

This paper formulates and solves optimization problems whose objective is to maximize the sum-throughput of wireless sensor networks with radio frequency (RF) energy harvesting (EH) and backscatter communication. The paper proposes and analyzes three protocols for maximizing the sum-throughput; namely, the time-division with downlink data decoding protocol, hybrid power splitting/data decoding protocol, and backscatter-enabled combination protocol. The time-division protocol optimizes a wireless powered communication network (WPCN) with a two-way communication link between the sensors and a hybrid access point. The hybrid protocol optimizes a WPCN with a power splitting downlink data decoding phase. The backscattered-enabled combination protocol optimizes a WPCN with a power splitting downlink data decoding phase and backscatter communication enabled sensors. Numerical results show that the hybrid and combination protocols deliver up to 14.5% and 30.5% increase in sum-throughput, respectively, compared with the reference time-switching RF EH protocol in a dynamic environment. The findings are significant for the future widespread adoption of WPCN systems powered by RF energy sources in the real world by increasing the amount of harvested energy and system achievable data rate.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Sum Secrecy Rate Maximization for Full-Duplex Two-Way Relay Networks Using Alamouti-Based Rank-Two Beamforming

Consider a two-way communication scenario where two single-antenna nodes, operating under full-duplex mode, exchange information to one another through the aid of a (full-duplex) multi-antenna relay, and there is another single-antenna node who intends to eavesdrop. The relay employs artificial noise (AN) to interfere the eavesdropper's channel, and amplify-forward (AF) Alamouti-based rank-two beamforming to establish the two-way communication links of the legitimate nodes. Our problem is to optimize the rank-two beamformer and AN covariance for sum secrecy rate maximization (SSRM). This SSRM problem is nonconvex, and we develop an efficient solution approach using semidefinite relaxation (SDR) and minorization-maximization (MM). We prove that SDR is tight for the SSRM problem and thus introduces no loss. Also, we consider an inexact MM method where an approximately but computationally cheap MM solution update is used in place of the exact update in conventional MM. We show that this inexact MM method guarantees convergence to a stationary solution to the SSRM problem. The effectiveness of our proposed approach is further demonstrated by an energy-harvesting scenario extension, and by extensive simulation results.

Controlling the renewable microgrid using semidefinite programming technique

Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crisis, renewable distributed energy resources (DERs) are going to be integrated in smart grids, which will make the energy supply more reliable and decrease the costs and transmission losses. Unfortunately, one of the key technical challenges in power system planning, control and operation with DERs is the voltage regulation at the distribution level. This problem stimulates the deployment of smart sensors and actuators in smart grids so that the voltage can be stabilized. The observation from the microgrid incorporating DERs is transmitted to the control center via wireless communication systems. In other words, the proposed communication infrastructure provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid state information collection, estimation and stabilization. Based on the communication infrastructure, we propose a least square based Kalman filter algorithm for state estimation and an optimal feedback control framework for stabilizing the microgrid states. Specifically, we propose to optimize the performance index by using semidefinite programming techniques in the context of smart grid applications. At the end, the efficacy of the developed approaches is demonstrated using a microgrid incorporating multiple DERs.

An Efficient Two-Step Power Allocation Method for OFDM-Based Two-Way Full-Duplex Links

An efficient two-step method to allocate power for orthogonal frequency division multiplexing-based full-duplex (FD) two-way communication links is proposed in this letter. We try to maximize sum data rate of the two-way link by allocating available power over each subcarrier at both FD transceivers with imperfect self-interference cancellation. This sum-rate optimization problem is formulated under total power constraint. In order to solve this problem, we first consider a one-subcarrier problem and derive the closed-form solution for power allocation. Based on this result, the multi-carrier optimization problem can be solved by our proposed two-step method. First, we allocate total power to each subcarrier optimally, and then we solve the power allocation problem for each subcarrier. Compared with direct dual decomposition method, numerical results indicate that our two-step method can obtain the same two-way link performance, however, with much lower computational complexity.

Channel Simulation via Interactive Communications

In this paper, we study the problem of channel simulation via interactive communication, known as the coordination capacity, in a two-terminal network. We assume that two terminals observe independent identically distributed (i.i.d.) copies of two random variables and would like to generate i.i.d. copies of two other random variables jointly distributed with the observed random variables. The terminals are provided with two-way communication links, and shared common randomness, all at limited rates. Two special cases of this problem are the interactive function computation studied by Ma and Ishwar, and the tradeoff curve between one-way communication and shared randomness studied by Cuff. The latter work had inspired Gohari and Anantharam to study the general problem of channel simulation via interactive communication stated above. However, only inner and outer bounds for the special case of no shared randomness were obtained in their work. In this paper, we settle this problem by providing an exact computable characterization of the multiround problem. To show this we employ the technique of output statistics of random binning that has been recently developed by the authors.

Local Utility Power Line Communications in the 3–500 kHz Band: Channel Impairments, Noise, and Standards

Future smart grid systems will intelligently monitor and control energy flows to improve the efficiency and reliability of power delivery. This monitoring and control requires low-delay, highly reliable communication between customers, local utilities, and regional utilities. A vital part of future smart grids is the two way communication links between smart meters at the customer sites and a (decentralized) command and control center operated by the local utility. To enable these two-way communication links, narrowband power line communication (PLC) systems operating in the 3-500 kHz band are attractive because they can be deployed over existing outdoor power lines. Power lines, however, have traditionally been designed for one-directional power delivery and remain hostile environments for communication signal propagation. In this article, we review signal processing approaches to model channel impairments and impulsive noise and mitigate their effects in narrowband PLC systems. We examine ways to improve the communication performance based on current and emerging standards.

Mutually exclusive data encoding for realization of a full duplexing self-steering wireless link using a retrodirective array transceiver

A two-way communication link is established between an interrogator and retrodirective antenna array. The link is realized by using standard time-domain modulation for data uplink to the retrodirective array, while outgoing data is coded onto the antenna polarization sense of the signal sent back to the interrogator. Using these noninterfering mutually exclusive modulations schemes, this system architecture is able to resolve the difficulty of dealing with the corruption of the outgoing retrodirected signal by the incoming interrogation signal inherent when using phase-conjugation mixers (PCMs). This paper will discuss the development of a novel PCM also used for polarization modulation and a polarization state detector for demodulation. Analysis of circuit design tolerances and affects of jammers on proper polarization detection is also shown. Demonstration of simultaneous data transfer is presented in simulation and measurement along with the array's automatic beam-steering ability.

Link activation protocols for a mobile communication network with directive/adaptive antennas

A noncentralized fully distributed mobile communication network with directive/adaptive antennas is proposed. The spatial and temporal synchronization of transmitting and receiving antenna beams is accomplished by employing the global positioning system information and a synchronized spatial search algorithm. The link setup between a communication transmitter (CT) and a communication receiver (CR) depends on the success of both forward and reverse links. A two-way communication link protocol is defined as a basic agreement for the link activation in the network. Two link search algorithms, for additive white Gaussian noise channels, are proposed for use in hostile communication environments as follows: (1) a sequential ascending search power algorithm using an optimal set of link power levels and (2) a sequential Bayesian search algorithm (SBSA) using Bayes rule to update the CR location information. The optimal SBSA can be obtained by using dynamic programming, but the computational complexity is unfeasibly high. The experimental results in which the CT uses a noncoherent matched filter receiver for spread-spectrum code acquisition and the unfriendly interceptor uses a wide-band energy detector for both link search algorithms are presented.