Symbol Error Rate Research Articles

Beamforming Design for Integrated Sensing and Communication Systems With Finite Alphabet Input

Joint beamforming for integrated sensing and communication (ISAC) is an efficient way to combine two functionalities in a system at the waveform level. This letter proposes a beamforming design for ISAC systems with finite alphabet signaling. We formulate a problem to maximize the minimum Euclidean distance (MMED) among noise-free received signal vectors under a sensing constraint and a given power constraint. To tackle the formulated optimization problem, we transform it into a semi-definite programming (SDP) and solve it by the semi-definite relaxation (SDR) method. Comprehensive comparisons with existing schemes show that our proposed beamforming offers lower symbol error rate (SER), higher mutual information, and also better sensing performance than existing ISAC beamforming designs.

Pace Time Modulation for Intra-Cardiac Communication

Although single-node leadless pacemakers eliminate several complications associated with subcutaneous pockets and leads, they have the significant limitation of only being able to perform single-chamber pacing. Multi-nodal pacing can outperform single-chamber pacing by providing a closer approximation of sinus rhythm but requires energy-efficient wireless communication to preserve synchrony. In this work, we propose a novel biocompatible communication concept wherein the pacemaker node located in the sinoatrial (SA) node stimulates the right atrium and simultaneously sends a synchronization message, encoded into the pacing time, to all other pacemaker nodes located at different sites in the heart. We refer to this concept as the Pace Time Modulation (PTM) method and assess its performance in terms of symbol error rate and achievable information rate. The analyses and numerical simulations indicate that PTM can be applicable in realistic scenarios, without deviating the heart rate from its normal range.

On the Error Performance of LoRa-Enabled Aerial Networks Over Shadowed Rician Fading Channels

UAVs can be used as aerial relays to provide communication services in remote uncovered areas or dense environments with occasional high capacity demands. However, due to the low power of Internet-of-Things (IoT) devices, UAV-based IoT applications, such as precision agriculture and environment monitoring, may experience high shadowing or equipment failure, which degrades the communications’ quality between IoT devices and their gateways. To tackle this issue, we consider the long range (LoRa) communication technology. Specifically, we investigate the performance of LoRA-enabled aerial communications, where a LoRa gateway communicates with a distant IoT device through the assistance of an amplify-and-forward (AF) aerial relay. Under the assumption of shadowed Rician fading channels, we characterize at first the end-to-end LoRa communication link. Then, we derive an exact symbol error rate expression for the underlying system model. Finally, numerical results are presented to corroborate the efficacy of our derived expressions and provide valuable insights into the error performance of LoRa-enabled aerial networks.

Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions

This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.

Simulation Analysis of 4G/5G OFDM Systems by Optimal Wavelets with BPSK Modulator

LTE (Long-Term Evolution) is one of the appropriate technologies to sever the requirement of the IoT (Internet of Things) together with mobile UE (User Equipment), because of priority services, high data rate, low latency, etc. LTE has received OFDM for the transmission because it meets the necessity of LTE-like range flexibility and empowers the cost gainful responses for the wide carriers. LTE offers big-data values, allows spectrum reframing, reduces cost, etc. We proposed a 4G/5G adopted OFDM based on the multi-equalizer and BPSK (Binary-Phase Shift Keying). The BER (Bit Error Rate) of the system is reduced through the optimal wavelet coefficient at the transmitter side. This optimal wavelet coefficient is obtained by applying a DWT (Discrete Wavelet Transform) to the input signal through a BFF (Binary Firefly) algorithm. The serial to parallel process is carried out through the BPSK modulation. At last, the multi-equalizer like MMSE (Minimized Mean Square Error) and MLE (Maximum Likelihood) is applied to the OFDM model to reduce the BER. The system is analyzed using the parameter like SER (Symbol Error Rate), spectral efficiency, MSE (Mean Square Error), and BER. The proposed methodology provides a better outcome than the other existing approaches.

Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels

Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters.

Performance enhancement of visible light communication (VLC) system incorporating WMZCC‐OCDMA codes and PDM‐QPSK‐DSP data encoding

SummaryMulti‐user optical code division multiplexing (OCDMA) in visible light communication (VLC) is an imminent domain for providing an even distribution of available bandwidth and improved security. Q4R1Zero cross‐correlation (ZCC) codes are widely reported, and improved performance is offered by them due to insignificant multiple access interference (MAI). However, practically, MAI exists in ZCC codes due to adjacent weights (W) in the code matrix rows for each user. Therefore, a novel ZCC code with a nonadjacent W is designed in this work to eradicate MAI limitations and is named weight managed ZCC codes (WMZCC). An indoor 100 Gbps/User OCDMA‐VLC system is realised using polarisation division multiplexed quadrature phase shift keying (PDM‐QPSK) and digital signal processing (DSP). Incorporation of DSP in the coherent receiver compensates the effects of dispersion and nonlinear effects and also performs phase estimation. Further, a state‐of‐the‐art comparison of WMZCC codes with diagonal identity matrix (DIM) and zero cross‐correlation resultant weight (ZCCRW) codes has been performed in terms of log symbol error rate (SER), Q factor, error vector magnitude percentage (EVM%) and log bit error rate (BER) log at different VLC lengths, Q4R1Transmitter half angles (THA), incidence half angles (IHA) and optical concentrators (cm2). Constellations are analysed with and without DSP, and according to the author's best knowledge, a 100 Gbps VLC system for five users covering 6 m at −3.42 log BER is demonstrated for the first time. Results revealed that weight management in WMZCC codes makes its performance improve as compared with ZCCRW and DIM codes.

A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications

Turbulence in natural environments affects the reliability of communication. In this paper, we propose a symmetric matrix-assisted multiple-input multiple-output (MIMO) maritime visible light communication (VLC) system to confront turbulence and improve reliability. In our design, by exploiting the repeatability of elements in the symmetric matrix, the matrix can expand the Euclidean distance between signals and effectively restrains the interference between signals, thus improving the reliability of maritime VLC systems. In addition, we derive the theoretical symbol error rate (SER) of the proposed scheme. Then, simulation results are provided to validate the theoretical SER. In addition, the SER performances of the proposed system are compared with the three benchmark schemes of repetitive coding (RC), spatial modulation (SM), and spatial multiplexing (SMP), and the transmission performances in VLC systems with different link distances, water qualities, and wind speeds are also investigated. The proposed symmetric matrix-assisted MIMO maritime VLC system can combat interferences effectively and enhance reliability performance.

Investigations on PAPR and SER Performance Analysis of OFDMA and SCFDMA under Different Channels

Code Division Multiple Access (CDMA) and Orthogonal Frequency Division Multiplexing are two multicarrier regulatory systems that have improved due to the growing demand in quick communication (OFDM). A frequency division multiplexing (FDM) plot called orthogonal frequency division multiplexing is utilized as a sophisticated multi carrier regulation approach.
 Due to its excellent spectrum efficiency and low data rate, the single carrier multiple access system (SC-FDMA) is a unique radio transmission technique that is now employed in long-term evolution (LTE) technology for uplink. When compared to OFDM, reduced peak-to-average power ratio (PAPR) and error rate technique. PAPR performance has been obtained using Matlab simulation. Techniques using variable numbers of subcarriers include SC-FDMA and OFDMA. Two various subcarrier assignment strategies have been proposed, distributed FDMA Localized FDMA and (DFDMA) (LFDMA). IFDMA, or interleaved FDMA, is a DFDMA special situation when DFT results have been distributed consistently and at the same distance. When comparing the SC-FDMA variants, we discover that Localized has a higher PAPR than interleaved (FDMA) (FDMA). We also talk about the Performance of LFDMA and IFDMA's SER (Symbol Error Rate) and found that localized (FDMA) performs better in the SER than interleaved (IFDMA) method.

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network.

The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition. In the simulation process, the pitch preference feature of piano playing is used as the identification basis, and the effectiveness of the algorithm is verified through four sets of experiments. The experimental results show that the average symbol error rate of the improved network model is reduced to 0.3234%, and the model training time is about 33.3% of the traditional convolutional recurrent neural network, which is optimized in terms of recognition accuracy and training time in single-class pitch feature. In the recommended method of multi-category evaluation of pitch features, the recognition accuracy of multi-category pitch features is 42.89%, which effectively improves the musical tone recognition rate.

Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System.

Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond.

Comprehensive performance analysis of hybrid FSO/RF space–air–ground integrated network

Free space optics (FSO) communication due to its large optical carrier bandwidth offers better data rates close to gigabits per second (GBPS) compared to radio frequency (RF) communication and can support seamless ubiquitous high-speed broadband connectivity, which is a significant requirement for sixth-generation (6G) communication. It is also envisioned that the data transmission in 6G communication will be over space–air–ground integrated network (SAGIN) to provide broadband global mobile connectivity. Thus, our main aim in this paper is to investigate the performance of a dual-hop (DH) system model utilizing high-altitude platform station (HAPS) as a relay station with FSO communication for SAGIN scenario, which is expected to be featured in 6G communication. Despite various advantages, HAPS-based FSO satellite communication (SATCOM) is undermined by several factors such as atmospheric turbulence, pointing errors, and atmospheric attenuation. To improve the system performance in terms of capacity and reliability, hybrid FSO/RF communication with RF link as a backup is utilized between ground station (GS) and HAPS. Further, comprehensive end-to-end system performance is investigated by deriving unified closed-form expressions for outage probability and average symbol error rate (SER). We also obtain approximate upper bound expressions for ergodic capacity and outage capacity. The asymptotic expressions are also derived to obtain the diversity order. Here, FSO and RF links are modelled using generalized Malaga distribution with non-zero boresight pointing errors and shadowed κ−μ distribution, respectively. Further, the closed-form expressions for optimum switching threshold signal-to-noise ratio (SNR) are derived and the obtained optimal SNR values are verified using a numerical optimization technique. Finally, the results show that the proposed DH SAGIN-based hybrid FSO/RF SATCOM system is more reliable as well as achieves higher capacity compared to the FSO-based SATCOM systems.

Performance analysis of multihop wireless systems with decode and forward relays over Fisher– Snedecor F fading channels

ABSTRACT By reducing the transmitting power at the base station and expanding the coverage range of wireless communication, cooperative communication has turned into a higher imperative. This article evaluates the outage probability, average symbol error rate (ASER) for coherent as well as noncoherent modulation pattern, ergodic capacity and throughput of a multihop communication technique over the Fisher–Snedecor F fading channels. The multihop transmission scheme utilises a source, a set of relays and the destination node. The intervening relays of the system are regenerative with decode and forward type. Each hop of the system undergoes Fisher–Snedecor F fading channels. The MGF-based approach is used to derive the ASER of the system. The outage probability and ASER performance upgrade with the advancement of fading parameter as well as shadowing parameter of the channel. The ergodic capacity decreases with the improvement in shadowing parameter and the number of hops. The throughput of the system rises in connection with the reduction of total hops. Simulations validate the correctness of the numerically appraised results.

Performance analysis of communications systems with radar interference and hardware impairment

The development of future wireless communications systems faces a big challenge of spectrum scarcity. Co-existence of radar and communications systems is thus of great interest. In this work, the performance of a communications system with hardware impairment (HWI) as well as interference from radar systems will be studied. The impact of radar interference, I/Q imbalance coefficients as well as channel state information (CSI) will be evaluated in terms of outage probability and symbol error rate. Simulation results prove that the proposed detectors provide explicit and conducive insights for further exploration of joint radar-communications designs.

An Improved Data-Driven Decision Feedback Receiver via Deep Unfolding

The proposed decision feedback receiver (DFR) is an end-to-end data-driven iterative receiver, and the performance gain is achieved from iterations. However, the mismatch problem between the training set and test set exists in the DFR training, and thus performance degradation, slow convergence speed, and oscillation are introduced. On the other hand, deep unfolding using parameter sharing is a practical method to reduce the model parameter number and improve the training efficiency, but the problem whether the parameter sharing will cause performance degradation is rarely considered. In this work, we generally discuss and analyze these two problems, and solution to solve the problem or conditions that the problem no longer exists is then introduced. We give the improvements to address the mismatch problem in the DFR, and thus we propose an improved-DFR via deep unfolding. The improved-DFRs with and without parameter sharing, namely, DFR-I and DFR-IS, are both developed with low computation complexity and model complexity and can be executed by parallel processing. Besides, the practical training tricks and performance analysis including computation complexity and model complexity are given. In the experiments, the improved-DFRs outperform the DFR in various scenarios, in terms of convergence speed and symbol error rate. The simulation results also show that the DFR-IS is easier to train, and the slight performance loss can be reduced by increasing model complexity, in comparison to DFR-I.

Communication Systems Performance at mm and THz as a Function of a Rain Rate Probability Density Function Model.

6G is already being planned and will employ much higher frequencies, leading to a revolutionary era in communication between people as well as things. It is well known that weather, especially rain, can cause increased attenuation of signal transmission for higher frequencies. The standard methods for evaluating the effect of rain on symbol error rate are based on long-term averaging. These methods are inaccurate, which results in an inefficient system design. This is critical regarding bandwidth scarcity and energy consumption and requires a more significant margin of effort to cope with the imprecision. Recently, we have developed a new and more precise method for calculating communication system performance in case of rain, using the probability density function of rain rate. For high rain rate (above 10 mm/h), for a typical set of parameters, our method shows the symbol error rate in this range to be higher by orders of magnitude than that found by ITU standard methods. Our model also indicates that sensing and measuring the rain rate probability is important in order to provide the required bit error rate to the users. This will enable the design of more efficient systems, enabling design of an adaptive system that will adjust itself to rain conditions in such a way that performance will be improved. To the best knowledge of the authors, this novel analysis is unique. It can constitute a more efficient performance metric for the new era of 6G communication and prevent disruption due to incorrect system design.

On the ASER performance of RF energy harvesting multiple relay networks

We present the average symbol error rate (ASER) of quadrature amplitude modulation (QAM) for radio frequency energy harvesting assisted dual-hop multiple amplify-and-forward (AF) relay network. In this relaying network, partial relay selection scheme is employed to select best relay among multiple relays, while selection combining is considered at destination to combine the signals coming from the direct path and the relaying path. In particular, we derive the exact ASER expressions of rectangular QAM (RQAM), hexagonal QAM and cross QAM schemes for the considered systems over independent and non-identically distributed Nakagami-m fading channels using cumulative distribution function method. Moreover, the asymptotic ASER expression of RQAM is determined to examine the system’s diversity order. Further, to verify the correctness of derived expressions, numerically evaluated results are compared with Monte Carlo simulations. Furthermore, the effects of fading parameter, number of relay, time switching ratio, and large scale fading are studied on the ASER performance.

A Comprehensive Technique to Design SCMA Codebooks

Sparse code multiple access (SCMA) is a potential candidate for improving spectral efficiency through overloading over resources. This letter proposes a comprehensive approach to achieve near-optimal SCMA codebooks designs for 150% and 200% overloading factors under various channel environments. This approach is based on a novel diversity analysis framework presented in this letter. Following this diversity analysis for the Rayleigh fading channel, we formulate a new key performance indicator (KPI) of the average symbol error rate (SER). The SER performance of an SCMA codebook is sensitive to KPIs. Thus, a suitable selection of the KPIs can improve performance. The proposed codebook design technique involves constellation rotation and dimensional permutation to optimize the KPIs. The proposed method can yield an SCMA codebook, which works equally well for uplink (UL) and downlink (DL) Rayleigh fading channels. This aspect of the proposed method eliminates the requirement of different designs for UL and DL. The SER is calculated through extensive Monte-Carlo simulations for all the proposed codebooks (with varying factors of overloading and alphabet sizes). The proposed codebooks outperform the state-of-the-art SCMA codebooks.

Secure Directional Modulation in RIS-Aided Networks: A Low-Sidelobe Hybrid Beamforming Approach

This letter considers the incorporation of secure directional modulation in reconfigurable intelligent surface (RIS)-aided communication networks. With elaborately selected antenna subsets, secure directional modulation generates randomized signals at undesired directions to assure physical-layer security. One major challenge of introducing RIS is that an additional beam, other than the user-specific beam, is required to align to the RIS. The interplay between the two beams can cause high sidelobe, which is non-trivial to address due to the discrete optimization in antenna subset selection. To tackle this challenge, we propose a novel cross-entropy iterative method to achieve low-sidelobe hybrid beamforming for secure directional modulation in RIS-aided networks. We use maximum sidelobe energy as the objective function to reduce the emission, and deploy Kullback-Leibler divergence as the criterion in the iterative selection of good candidate antenna subsets. Simulation results illustrate that the proposed scheme can reduce the sidelobe energy by 8 dB, and the symbol error rate of the eavesdropper maintains at 0.75 (0.875) for QPSK (8PSK) even when its signal-to-noise ratio increases.

Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environments