Search Results (52)

This paper investigates the error rate performance of simultaneous wireless information and power transfer (SWIPT) systems employing opportunistic amplify-and-forward (OAF) relaying under Rayleigh fading conditions. To support both data forwarding and energy harvesting at relays, a power splitting (PS) mechanism is applied. We derive exact and asymptotic symbol error rate (SER) expressions using moment-generating function (MGF) methods, providing analytical insights into how the power splitting ratio $\rho$ and the quality of source–relay (SR) and relay–destination (RD) links jointly affect system behavior. Additionally, we propose a novel approximation that interprets the SWIPT-OAF configuration as an equivalent non-SWIPT OAF model. This enables tractable performance analysis while preserving key diversity characteristics. The framework is extended to include scenarios with partial channel state information (CSI) and $N_{\mathrm{th}}$ best relay selection, addressing practical concerns such as limited relay availability and imperfect decision-making. Extensive simulations validate the theoretical analysis and demonstrate the robustness of the proposed approach under a wide range of signal-to-noise ratio (SNR) and channel conditions. These findings contribute to a flexible and scalable design strategy for SWIPT-OAF relay systems, making them suitable for deployment in emerging wireless sensor and internet of things (IoT) networks. Full article

In this work, we investigate a new design strategy for the implementation of a deep neural network (DNN)-based limited-feedback relay system by using conventional filters to acquire training data in order to jointly solve the issues of quantization and feedback. We aim to maximize the effective channel gain to reduce the symbol error rate (SER). By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. We compare our proposed system to a Grassmannian codebook system to show that our system outperforms its benchmark in terms of SER. Full article

Optical communication is a critical technology for future deep space exploration, offering substantial advantages in transmission capacity and spectrum utilization. This paper establishes a comprehensive theoretical framework for avalanche photodiode (APD)-based deep space optical uplink communication under combined channel impairments, including atmospheric and coronal turbulence induced beam scintillation, pointing errors, angle-of-arrival (AOA) fluctuations, link attenuation, and background noise. A closed-form analytical channel model unifying these effects is derived and validated through Monte Carlo simulations. Webb and Gaussian approximations are employed to characterize APD output statistics, with theoretical symbol error rate (SER) expressions for pulse position modulation (PPM) derived under diverse impairment scenarios. Numerical results demonstrate that the Webb model achieves higher accuracy by capturing APD gain dynamics, while the Gaussian approximation remains viable when APD gain exceeds a channel fading-dependent gain threshold. Key system parameters such as APD gain and field-of-view (FOV) angle are analyzed. The optimal APD gain significantly influences the achievement of optimal SER performance, and angle of FOV design balances AOA fluctuations tolerance against noise suppression. These findings enable hardware optimization under size, weight, power, and cost (SWaP-C) constraints without compromising performance. Our work provides critical guidelines for designing robust APD-based deep space optical uplink communication systems. Full article

Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions. Full article

In this paper, an adaptive frequency-domain block equalizer (FDBE) implementing the adaptive moment estimation (Adam) algorithm is proposed for mode-division multiplexing (MDM) optical fiber communication systems. By packing all frequency components into frequency-dependent blocks of a specified size $B$, we define an adaptive equalization matrix to simultaneously compensate for multiple frequency components at each block, which is computed iteratively using the Adam, recursive least squares (RLS) and least mean squares (LMS) algorithms. Simulations show that the proposed FDBE using the Adam algorithm outperforms those using the LMS and RLS algorithms in terms of adaptation speed and symbol error rate (SER) performance. The FDBE using the Adam algorithm with $B=1$ has the fastest adaption time, requiring about $n_{tr}=100$ and $n_{tr}=900$ less training blocks than the RLS algorithm at the SER of $3.8\times {10}^{-3}$ for the accumulated mode-dependent loss (MDL) of $\xi =1 \mathrm{d}$B and $\xi =5 \mathrm{d}$B, respectively. The Adam algorithm with $B=16$ and $B=8$ has $0.4 \mathrm{d}$B and $0.3 \mathrm{d}$B SNR better than the RLS algorithm with $B=4$ for MDL and $\xi =1 \mathrm{d}$B and $\xi =55 \mathrm{d}$B, respectively. Full article

Forests play a critical role in maintaining global ecological balance, regulating climate, and supporting biodiversity. Effective forest management and monitoring relies on the deployment of large-scale wireless sensor networks (WSNs) for real-time data collection, enabling the protection of ecosystems and the early detection of environmental changes. However, such massive deployments pose serious challenges with increasingly scarce radio resources. Sparse code multiple access (SCMA), a non-orthogonal multiple access (NOMA) technique, has been identified as a promising solution for facilitating wireless communications among numerous distributed sensors in large-scale WSNs with improved spectral efficiency. This is essential for application scenarios involving a substantial number of terminal devices, including forest monitoring and management. Codebook design is a critical issue for SCMA systems. It is closely related to the detection performance at the receiver, which in turn has a direct effect on the communication coverage or quality of service (QoS) for the terminal devices. This paper investigates the symbol error rate (SER) performance of SCMA systems over AWGN channels and derives its theoretical upper bound. The optimization objectives for each stage of codebook design are mathematically analyzed for a single resource element (RE), a single device, and multi-device, multi-RE scenarios. On this basis, a lightweight stepwise codebook design scheme is proposed in this paper. Simulation results demonstrate that the proposed codebooks can maintain fairness among devices while guaranteeing detection performance. Full article

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work. Full article

Vertical cavity surface-emitting laser (VCSEL)-based optical interconnects (OI) are crucial for high-speed data transmission in data centers, supercomputers, and vehicles, yet their performance is challenged by harsh and fluctuating thermal conditions. This paper addresses these challenges by integrating an ordinary differential equation (ODE) solver within the VCSEL communication chain, leveraging the adjoint method to enable effective gradient-based optimization of pre-equalizer weights. We propose a machine learning (ML) approach to optimize feed-forward equalizer (FFE) weights for VCSEL transceivers, which significantly enhances signal integrity by managing inter-symbol interference (ISI) and reducing the symbol error rate (SER). Full article

Artificial intelligence (AI) is revolutionizing multiple-input-multiple-output (MIMO) technology, making it a promising contender for the coming sixth-generation (6G) and beyond-fifth-generation (B5G) networks. However, the detection process in MIMO systems is highly complex and computationally demanding. To address this challenge, this paper presents an optimized AI-based signal detection method known as AIDETECT-2 which is based on feed forward neural network (FFNN) for MIMO systems. The proposed AIDETECT-2 network model demonstrates superior efficiency in signal detection in comparison with conventional and AI-based MIMO detection methods, particularly in terms of symbol error rate (SER) at various signal-to-noise ratios (SNR). This paper thoroughly explores various signal detection aspects using FFNN, including the design of system architecture, preparation of data, training processes of the network model, and performance evaluation. Simulation results show that the proposed model demonstrates a significant performance improvement ranging between 13.75% to 99.995% better SER compared to the best conventional method and also achieved between 56.52% to 97.69 better SER compared to benchmark AI-based MIMO detectors at 20 dB SNR for given MIMO scenarios respectively. It also presented the computational complexity analysis of different conventional and AI-based MIMO detectors. We believe that this optimized AI-based network model can serve as a comprehensive guide for deploying deep-learning (DL) neural networks for signal detection in the forthcoming 6G wireless networks. Full article

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches. Full article

This two-part work develops a comprehensive toolbox for the statistical characterization of nonlinear distortions of DAC-generated signal constellations to be transmitted over communication links, be they electronic (wireline, wireless) or photonic, Mach–Zehnder modulator (MZM)-based optical interconnects in particular. The all-analytic toolbox developed here delivers closed-form expressions for the second-order statistics (means, variances) of all relevant constellation metrics of the DACs’ building blocks and of DAC-driven MZM-based optical transmitters, all the way to the slicer in the optical receivers over a linear channel with coherent detection. The key impairment targeted by the model is the random current mismatch of the ASIC devices implementing the DAC drivers. In particular the (skew-)centrosymmetry of the DAC metrics is formally derived and explored. A key applicative insight is that the conventional INL/DNL (Integral NonLinearity/Differential NonLinearity) constellation metrics, widely adopted in the electronic devices and circuits community, are not quite useful in the context of communication systems, since these metrics are ill-suited to predict communication link statistical performance. To rectify this deficiency of existing electronic DAC metrics, we introduce modified variants of the INL|DNL, namely the integral error vector (IEV) and the differential error vector (DEV) constellation metrics. The new IEV|DEV represent straightforward predictors of relevant communication-minded metrics: error vector magnitude (EVM) treated here in Part I, and Symbol/Bit Error-Rates (SER, BER) treated in the upcoming Part II of this paper. Full article

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle’s movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle’s starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle’s position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance. Full article

An end-to-end testbed for In-phase and Quadrature (I/Q) Imbalance (IQI) communication systems based on Software-Defined Radio (SDR) is presented. The scenario under consideration is a Single-Input–Single-Output (SISO) single-carrier communication where the transmitter is heavily affected by IQI, whose effects are mitigated through digital signal processing at the receiver. The presented testbed is highly configurable, enabling the testing of different communication and IQI parameters. Crucial insights into the practical implementation of IQI mitigation techniques, specifically through the use of asymmetric signaling at the receiver, are provided. Initially, a detailed description of the mathematical framework is given. This framework serves as the foundation for the subsequent discussion on system implementation, effectively bridging the gap between research on IQI mitigation and its practical application in single-carrier architectures. Over-The-Air (OTA) Symbol Error Rate (SER) measurements for different constellations validate the receiver design and implementation. The source code of the presented testbed is publicly available. Full article

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed. Full article

Photonic integrated circuits (PIC) devices are impacted by fabrication tolerances and therefore, prior knowledge of such variations could improve the PIC fabrication process and overall yield. This paper presents a method for predicting the fabrication impacts on a telecommunication optical digital to analog converter (oDAC)-based pulse amplitude modulator level four (PAM-4) transmitter as a case study where the certainty of this passive device is subjected to random variation. Our findings allow us to estimate the production yield in a fabrication scenario using the symbol error rate (SER) benchmark and this contributes to the study of the viability of oDAC PAM-4 transmitters to replace conventional electrical digital to analog converter (eDAC) PAM-4 transmitters. Full article