Uplink Communication Research Articles

Making the Most of Sporadic Feedback: Low-Complexity Application Layer Coding for Data Recovery in the Internet of Things

We propose application-layer coding schemes to recover lost data in delay-sensitive uplink (sensor-to-gateway) communications in the Internet of Things. Built on an approach that combines retransmissions and forward erasure correction, the proposed schemes' salient features include low computational complexity and the ability to exploit sporadic receiver feedback for efficient data recovery. Reduced complexity is achieved by keeping the number of coded transmissions as low as possible and by devising a mechanism to compute the optimal degree of a coded packet in O(1). Our major contributions are: (a) An enhancement to an existing scheme called windowed coding, whose complexity is greatly reduced and data recovery performance is improved by our proposed approach. (b) A technique that combines elements of windowed coding with a new feedback structure to further reduce the coding complexity and improve data recovery. (c) A coded forwarding scheme in which a relay node provides further resilience against packet loss by overhearing source-to-destination communications and making forwarding decisions based on overheard information.

Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization

In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.

Outage probability minimization for vehicular networks via joint clustering, UAV trajectory optimization and power allocation

In this paper, we consider the vehicular uplink communication scenario where vehicles in the same lane travel in a direction within the coverage of the base station. Due to the long communication distance and poor signal quality at the cell edge, an unmanned aerial vehicle (UAV) is used as a mobile relay to improve the communication quality. Our goal is to minimize the communication outage probability by jointing vehicle clustering with UAV trajectory optimization and power allocation of each node. Since the cluster head position, UAV position and power of each node are coupled, the primary complex problem is divided into three sub-problems namely vehicle clustering, trajectory optimization and power allocation. Firstly, the improved K-means clustering algorithm is used for vehicle clustering by addressing the minimum–maximum distance criterion. In order to adapt to the changing network topology, the cluster head vehicle is determined dynamically. Then the joint Lagrangian gradient ascent method is used to determine the UAV position and the power of each vehicle through alternate iterations. The simulation results show that the proposed joint optimization scheme (J-CTOPA) is better than the equal power scheme with fixed relay position (FR-EP) and the power allocation scheme with cruise relay trajectories (CT-PA). The results also show that faster convergence and stronger stability can be achieved while the outage probability is reduced.

Research on uplink performance of MIMO terrestrial-satellite laser communication based on OSM-MBM joint modulation

This paper proposes a multiple-input multiple-output (MIMO) terrestrial-satellite laser communication uplink system based on optical spatial modulation with medium-based modulation (OSM-MBM) joint modulation. The Málaga(M) distribution is used to describe the combined effects of near-ground turbulent light intensity scintillation, beam wander and angle of arrival fluctuation. The closed expression of the bit error rate (BER) of the OSM-MBM joint modulation MIMO terrestrial-satellite laser communication uplink is derived. The simulation analyzes the influence of transmitting radius, receiving aperture, beam divergence, zenith angle and signal-to-noise ratio on the system error performance, and compares it with traditional modulation, media-based modulation and optical space modulation. Finally, the experiment is verified by Monte Carlo. This research provides a theoretical reference for the engineering realization of the modulation technology for the MIMO terrestrial-satellite laser communication uplink system.

Digital Predistortion for Dual-Linearly Polarized Millimeter-Wave Phased Array User Equipment

To accommodate the ongoing user’s data explosion, many approaches like polarization multiplexing are proposed to increase the uplink communication capacity with limited bandwidth in user equipment (UE). This article proposes a novel dual-linearly polarized digital predistortion (DLP-DPD) scheme for DLP millimeter-wave (mmWave) phased array UE. Different from conventional single-polarized arrays, the DPD here not only aims to deal with the nonlinear distortion caused by power amplifiers (PAs) but also the cross-polarization interference which is a specific distortion in DLP phased array system. Theoretical analysis and system modeling of DLP phased array is presented, and a DLP-DPD model is proposed according to the derivation. Furthermore, in order to reduce the complexity of the DPD module, the proposed DLP-DPD scheme only needs to update a small part of coefficients when the user moves within a beam coverage. To validate the proposed DLP-DPD scheme, experiments were carried out on a 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 4 DLP mmWave phased array at 26.5 GHz. The experimental results show that DLP-DPD scheme can achieve good performance in DLP phased array dynamic scenario, dealing with the nonlinearity and cross-polarization interference well.

A Rateless 16QAM Scheme for IoT Uplink Communications

How to achieve a high transmission rate at a low cost of energy consumption in a wide range of channel variations is a critical challenge in Internet of Things (IoT) applications. In this article, a rateless 16 quadrature amplitude modulation (R-16QAM) scheme for IoT uplink communications is proposed to deal with this problem. The R-16QAM transmitter consists of an encoder, a random bit selector, and a tailored modulation mapper. Different from the sequential bits selection and Gary mapping in conventional 16QAM modulation, bits are randomly selected and mapped into a 16QAM symbol by a weighted summation of selected bits in R-16QAM. The random bits selection introduces correlation among modulated symbols, enabling the transmitter to work in a rateless manner, i.e., the transmitter keeps generating and sending R-16QAM symbols until a feedback of successful decoding is received from the receiver. It means that the number of symbols transmitted varies with channel variations. In this way, the proposed R-16QAM can automatically adjust to channel conditions and work well in a wide range of channel conditions. The weighted-summation mapping can be illustrated by a “Tanner graph,” based on which we develop a low complexity iterative demodulation algorithm to improve receiver performance. The simulations show that compared with narrow band Internet of Things (NB-IoT) standard and Turbo coded light-weight rate compatible modulation (TLRCM) for IoT uplink communications, the proposed R-16QAM scheme can consistently achieve a lower energy consumption in a signal-to-noise-ratio (SNR) range of −10–15 dB. Moreover, it can achieve a maximum transmission throughput of 3.96 bits/s/Hz at SNR=15 dB while only 0.66 and 1.7 bits/s/Hz are achieved in NB-IoT and TLRCM, respectively. These advantages enable the proposed R-16QAM scheme to be particularly suitable to IoT application scenarios where nodes work with limit energy resources under a large range of channel variations.

Atmospheric Effects on Satellite–Ground Free Space Uplink and Downlink Optical Transmissions

Free space optical (FSO) communications have the potential to be one of the most essential technologies for solving the high-bandwidth demands of communications between satellites and ground stations. In this study, we examine the impact of the atmosphere on satellite–ground FSO uplink and downlink communications. To consider diverse atmospheric conditions on both uplink and downlink, we derive FSO channel elements such as the fog attenuation coefficient, refractive index parameter, coherence length, turbulence model, and angle-of-arrival fluctuation. Unlike conventional work, we provide FSO channel analysis based on variations in the Fried parameter, zenith angle, scintillation index, and Rytov variance. Using simulation results from the optical settings, we examine the influence on channel performance of conditions such as atmospheric attenuation and intensity fluctuation. Based on this examination, we determine that 1550 nm is the preferred wavelength for both uplink and downlink FSO channels to mitigate the impact of turbulence and that larger receiver apertures lessen angle-of-arrival changes.

Enhancing underwater Acoustic Identification Tag design using concurrent wireless ultrasonic power transfer and backscatter communication

Improvements in undersea localization and navigation of Autonomous Underwater Vehicles (AUVs) is critical. However using conventional active transponders as navigation aids is limited by powering constraints. Passive backscatter devices such as Acoustic Identification (AID)Tags are an alternate solution to this energy limitation [Satish et al., JASA 149 (2021)], but offer limited information content. To address this limitation, adding a wireless ultrasonic power transfer capability to these passive AID tags can be used to enable backscatterer uplink communication between the AID tag and an interrogating compact high-frequency SONAR system (which can be mounted on an AUV). Our design comprises a piezoelectric transducer impedance matched to a connected electrical load across two frequency ranges: a narrowband energy harvesting range, and a broadband data range. Onboard electronics harvest acoustic energy in the narrowband to power an onboard microcontroller, which can be used to simultaneously modulate the transducer broadband acoustic impedance by switching the electrical load connected to transducer during backscatter uplink communication. Water tank experiment using custom transducers, operating either around 1 MHz or 350 kHz, were conducted to quantify-amongst others-the operational source to tag distance, system power requirements and achievable communication data rates using the proposed concurrent ultrasonic power and data transfer approach.

STAR-RIS Integrated Nonorthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization

This paper integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omni-directional coverage extension. To capture the impact of non-ideal wireless channels on AirFL, a closed-form expression for the optimality gap (a.k.a. convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled non-convex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that i) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications, ii) our algorithms achieve faster convergence rate on IID and non-IID settings compared to existing baselines, and iii) both the spectrum efficiency and learning performance is significantly improved with the aid of the well-tuned STAR-RIS.

A practical radio frequency fingerprinting scheme for mobile phones identification

Radio frequency fingerprint (RFF) has attracted a remarkable surge of attention to wireless transmitter identification due to the inimitable hardware imperfection. However, most RFF-based schemes devised in controlled environments are unable to reach the claimed performance in real-world scenarios. This paper proposes a practical RFF identification method for mobile phones in Global System for Mobile (GSM) system. Specifically, the instantaneous amplitude of near-transient part in normal burst (NB) is regarded as RFF that extracted from up-link communications. In addition, an anomaly filtering and stacking (AFS) method is introduced to obtain stable RFF in Time Division Multiple Access (TDMA) system. Lastly, the impacts of frequency point and sending power variations on RFF are fully dissected in comparison experiments. Experiments on 10 mobile phones show that the proposed RFF scheme yields 99.17% True Acceptance Rate (TAR) in real wireless environments. Experiments also show that the varying transmission power decreases the accuracy of RFF identification.

Performance evaluation of aeronautical uplink/downlink free-space optical communication system with adaptive optics over gamma–gamma turbulence channel

In this study, we analyze the effect of adaptive optics corrections on the performance of an aeronautical free-space optical (FSO) system with bidirectional slant path uplink and downlink communication channels. The aeronautical FSO communication (FSOC) system operates in a gamma–gamma atmospheric turbulence channel and employs adaptive optics corrections for the distorted wave front of the Gaussian beam wave. The modulation type of the aeronautical FSOC system is chosen to be M-ary phase-shift-keying-subcarrier intensity modulation and the type of the employed photodetector is positive-intrinsic-negative. In analysis, the effect of system parameters such as zenith angle, the height of transmitter/receiver on the ground, M-ary level, filter bandwidth, link distance, and the adaptive optics correction modes on bit-error-rate are demonstrated.

Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.

Joint Device Selection and Power Control for Wireless Federated Learning

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</i> scheme with error-free model exchange and full device participation.

Design of V2X-Based Vehicular Contents Centric Networks for Autonomous Driving

Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEE’s Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPP’s New Radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this paper, we define and design a vehicular network architecture compliant with 5G core networks to enable and support autonomous driving. As a validation example, a high-definition map needs to contain the context of trajectory for localization and planning of autonomous driving vehicles. We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of the proposed method via network simulations and our autonomous driving vehicle on the road. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.

Research on the performance of multiple-input/multiple-output space-frequency block coded system with exponential Weibull distribution in the satellite laser communication

We propose an orthogonal frequency division multiplexing multiple-input/multiple-output space-frequency block-coded (MIMO-SFBC) system based on the exponential Weibull distribution model for uplink and downlink satellite laser communication. Considering the different effects of atmospheric turbulence on the laser beam, the closed expression of bit error rate (BER) was derived for the proposed system. The relationship between the BER and signal-to-noise ratio was analyzed under uplink and downlink, respectively, and compared with the single-input/single-output and space-time block-coded systems. Simultaneously, the effects of different numbers of transmitting and receiving antennas on the selection of zenith angle, receiving aperture, transmitting radius, and beam divergence of the MIMO-SFBC system were also studied. Finally, the Monte Carlo method was used to verify the experimental data. This study provides a theoretical reference for the engineering implementation of MIMO coding technology in satellite laser communication systems.

Improving the Sum Rate and Fairness of MIMO Downlink Communications

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes.

Adaptive Attitude Control of a Rigid Body With Input and Output Quantization

In this article, the adaptive attitude-tracking problem of a rigid body is investigated, where the input and output are transmitted via a network. To reduce the communication burden in a network, a quantizer is introduced in both uplink and downlink communication channels. An adaptive backstepping-based control scheme is developed for a class of multiple-input and multiple-output (MIMO) rigid body systems. The proposed control algorithm can overcome the difficulty to proceed with the recursive design of virtual controls with quantized output vector, and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector output quantization and input quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the effectiveness of the proposed control scheme.

Communication-Efficient and Byzantine-Robust Differentially Private Federated Learning

Federated learning, as a novel paradigm of machine learning, is facing a series of challenges such as efficiency, privacy and robustness. The recently proposed EF-DP- SIGNSGD provides theoretical privacy protection for SIGNSGD with majority vote but weakens the capability to resist Byzantine attacks to some extent. To overcome this shortcoming and further greatly improve the communication efficiency, a new method called PCS-DP- SIGNSGD is proposed via using parallel compressed sensing. Simulation and analysis demonstrate that compared with EF-DP- SIGNSGD, PCS-DP- SIGNSGD can match or even improve the accuracy and enjoy stronger Byzantine robustness with 50% to 80% improvement in the uplink communication efficiency.

Computation Efficiency Optimization for RIS-Assisted Millimeter-Wave Mobile Edge Computing Systems

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

On Estimating the Location and the 3-D Shape of an Object in an Indoor Environment Using Visible Light

The recent interest in using visible light as a means of communication has opened up possibilities for using visible light for other applications as well, such as indoor positioning. Visible light offers higher bandwidth, immunity from electromagnetic radiation, and most importantly it can be seamlessly integrated into the existing lighting infrastructure. This paper proposes a visible light based positioning model for estimating an object’s three-dimensional (3-D) parameters such as height and radius in addition to location in an indoor environment. The model is built using neural networks (NN), trained by simulating numerous multiple object scenarios in an indoor environment. It also takes into account the shadowing effects so it can be implemented in a multiple obstacle environment. The proposed algorithm has numerous applications, such as positioning assisted communication, suspicious object monitoring and surveillance in an indoor environment. The proposed model achieves a location accuracy of 5.4 cm, which could be further improved to 4 cm at the expense of extra hardware. The accuracy in measuring the height and radius of the objects using the proposed framework is observed to be 4.39 cm and 1.27 cm, respectively. In addition, we also propose a methodology to optimize the power distribution to the light-emitting-diodes (LED’s) in order to get the optimum location accuracy while maintaining the total power constraint on the system. This method could be utilized to update the power allocated to the LEDs by exploiting the current location of the objects in the room to improve communication uplink by employing visible access points (VAP’s).