Symbol Error Rate Performance Research Articles

Maximum-Likelihood-Based Performance Enhancement of Clipped and Filtered OFDM Systems With Clipping Noise Cancellation

Clipping and filtering (CAF) technique is known as a simple and effective approach that reduces the peak-to-average power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) signals at the cost of increasing in-band distortion. Clipping noise cancelation (CNC) can compensate for the performance degradation due to the in-band distortion caused by CAF, especially when the number of subcarriers is large, but its achievable symbol error rate (SER) performance is in general worse than that of the unclipped OFDM systems. In this letter, we propose an additional compensation scheme based on the maximum likelihood (ML) metric. We reveal that the proposed approach can even outperform the unclipped OFDM systems, only with moderate increase of complexity from the conventional CNC. Furthermore, we demonstrate that significant gain resulting from the diversity effect can be achieved in the case of frequency-selective Rayleigh fading channels.

Analysis of Variable Bit Rate Spread FBMC-Based Integrated Satellite–Terrestrial Broadcast System

For the joint impact of carrier frequency offset (CFO) and phase noise (PHN), the exact closed-form signal-to-interference-noise ratio (SINR) expressions are derived for novel Walsh Hadamard (WH)-spread filterbank multicarrier (SFBMC) transmission scheme in integrated satellite–terrestrial broadcast system (ISTBS). For this evaluation, variable bit rates (VBR)s for WH-SFBMC are considered for transmission in 3-state Fontan land mobile satellite suburban environment. Further, symbol error rate (SER) and spectral efficiency are analyzed based on the obtained SINR expressions. Such analyses are conducted to support VBR quality of service (QoS) in orthogonal frequency division multiplexing (OFDM)-based digital video broadcasting standards utilizing ISTBS. Furthermore, pilot-based channel estimation error variance is evaluated for VBR WH-SFBMC. Later, block-by-block system computational complexity of VBR WH-SFBMC, perfect reconstruction (PR)-filterbank multicarrier (FBMC), and OFDM schemes are evaluated. It is shown that, VBR WH-SFBMC is rate-dependent and relatively more complex system than single rate PR-FBMC and OFDM. However, the SER performance of WH-SFBMC is illustrated to outperform PR-FBMC and OFDM significantly even for higher bit rates in presence of CFO and PHN. Moreover, simulation and analytic results are in close approximation with each other. Thus, a trade-off exists between achieved error performance and system complexity.

Structured DNN-Based Receiver for Millimeter-Wave MIMO With Nonlinear Distortions

This work deals with the combined effect of nonlinear distortions and inter-channel interference in millimeter wave multi-input multi-output (MIMO) communications. Deep neural networks (DNNs) can be used to handle the effect, but they often require a large number of pilot symbols, hindering their applications. With the aim of online training using a relatively small number of pilot symbols, we design a deep neural network (DNN) architecture carefully, which consists of a fully connected linear hidden layer and a non-fully connected nonlinear hidden layer. The linear hidden layer is used to deal with the co-channel interference and the nonlinear hidden layer is used to handle the nonlinear distortions. Moreover, the parameters of the DNN are properly tied to reduce the number of independent parameters. With such a DNN, the receiver is much efficient in terms of training overhead and symbol error rate performance, compared to conventional (DNN-based) techniques. Simulation results demonstrate the superiority of the proposed DNN-based receiver.

A Better Than Alamouti OSTBC for MIMO Backscatter Communications

Backscatter communications have received increasingly attention in Internet of Things (IoT) due to its advantages of requiring no internal battery and almost zero maintenance. However, the drawback of backscatter communications is that, its channel fades deeper than conventional channel. To improve the performance, multiple-input multiple-output (MIMO) techniques have been investigated for backscatter communications. By considering duty cycle, a particular behavior of backscatter communications, as a new performance criterion, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> orthogonal space-time block code for MIMO backscatter communications. The new code is a rotation of the classical Alamouti code, and in MIMO backscatter channels, this simple rotation will incur an improved duty cycle for both linear and nonlinear energy harvester models, as well as a same, or even better symbol error rate performance. We provide both rigorous mathematical proofs and numerical simulations to show the above results. An interesting thing is that, this new code does not outperform Alamouti code in conventional MIMO channels, which indicates that the space-time code design criteria for conventional channels is not the optimal one for backscatter channels, and existing space-time codes should be reconsidered and/or redesigned to achieve a higher performance in MIMO backscatter communications.

Deep Learning-Based Decoding and AP Selection for Radio Stripe Network

Cell-Free massive MIMO (mMIMO) offers promising features such as higher spectral efficiency, higher energy efficiency and superior spatial diversity, which makes it suitable to be adopted in beyond 5G (B5G) networks. However, the original form of Cell-Free massive MIMO requires each AP to be connected to CPU via front haul (front-haul constraints) resulting in huge economic costs and network synchronization issues. Radio Stripe architecture of cell-free mMIMO is one such architecture of cell-free mMIMO which is suitable for practical deployment. In this paper, we propose DNN Based Parallel Decoding in Radio Stripe (DNNBPDRS) to decode the symbols of User Equipments (UEs) in the uplink in a parallel fashion to reduce computational complexity by reducing delay in processing. Moreover, to solve the issue of Access Point (AP) selection in radio stripe networks, we propose a Channel link-based AP selection (CLBAPS) algorithm to choose the best APs in terms of channel link quality. The proposed DNNBPDRS framework not only improves Symbol Error Rate (SER) performance when compared to counterparts but is also proved to be comparatively far lesser computational complex. Moreover, the numerical result indicates the proposed AP selection algorithm CLBAPS performs better than random selection of AP in radio stripe networks.

Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights. For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver. The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.

A Novel Approach for Cancelation of Nonaligned Inter Spreading Factor Interference in LoRa Systems

Long Range (LoRa) has become a key enabler technology for low power wide area networks. However, due to its ALOHA-based medium access scheme, LoRa has to cope with collisions that limit the capacity and network scalability. Collisions between randomly overlapped signals modulated with different spreading factors (SFs) result in inter-SF interference, which increases the packet loss likelihood when signal-to-interference ratio (SIR) is low. This issue cannot be resolved by channel coding since the probability of error distance is not concentrated around the adjacent symbol. In this paper, we analytically model this interference, and propose an interference cancellation method based on the idea of segmentation of the received signal. This scheme has three steps. First, the SF of the interference signal is identified, then the equivalent data symbol and complex amplitude of the interference are estimated. Finally, the estimated interference signal is subtracted from the received signal before demodulation. Unlike conventional serial interference cancellation (SIC), this scheme can directly estimate and reconstruct the non-aligned inter-SF interference without synchronization. Simulation results show that the proposed method can significantly reduce the symbol error rate (SER) under low SIR compared with the conventional demodulation. Moreover, it also shows high robustness to fractional sample timing offset (STO) and carrier frequency offset (CFO) of interference. The presented results clearly show the effectiveness of the proposed method in terms of the SER performance.

Analysis of symbol error rate for intelligent reflecting surface aided communications

The recently proposed intelligent reflecting surface (IRS) is considered as a promising technology to combat the propagation distance problem in the future communications. However, due to the lack of quantitative analysis and IRS selection scheme from the perspective of symbol error rate (SER) performance, the performance analysis method of asymptotically tight approximation SER is proposed. Firstly, a communication system model aided by an IRS is established. Then, through the reasonable reconstruction between the reflection coefficient and the link coefficient of IRS, the SNR on the forwarding link of IRS satisfies the harmonic mean form. Finally, based on the calculation method of moment generating function (MGF), the asymptotically tight approximation SER formula of the system with simple structure is derived. The simulation results show that, the asymptotically tight approximation SER formula can accurately describe the system SER performance under the low SNR condition. Before the number of IRS reflection unit increases to a certain value, the system SER performance can be effectively improved. Compared with the AF multi-relay system, IRS aided system has the slightly better SER performance only in the case of low SNR. Our work will provide the important theoretical basis for the IRS selection in practical communications.

A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond (also called 6G) to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets (typically sporadic and of short size) over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station (BS) scans the eMBB traffic symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability, 4) and the proposed strategy has polynomial time complexity making it an efficient solution to be used in practice.

Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM

Power domain non-orthogonal multiple access (PD-NOMA) is one of the most perspective multiplexing technologies that allows improving the capacity of actual networks. Unlike orthogonal multiple access (OMA), the PD-NOMA non-orthogonally schedules multiple users in the power domain in the same orthogonal time-spectrum resource segment. Thus, a non-orthogonal multiplexed signal is a combination of several user signals (usually, modulation and coding schemes (MCS) based on quadrature amplitude modulation) with different power weights. The symbol error rate (SER) and bit error rate (BER) performances are one of the main quality characteristics of any commutation channel. The issue is that a known analytical expression for BER and SER calculation for conventional OMA cannot be applied in terms of the PD-NOMA. In the following work, we have derived the SER and BER analytical expressions for gray-coded square quadrature amplitude modulation (QAM) user channels that are transmitted in two-user PD-NOMA channel under additive white Gaussian noise (AWGN). Through the simulation, the verification of the provided expressions is presented for four multiplexing configurations with various user power weights and QAM order combinations.

Symbol Error Analysis of Amplify-and-Forward Based Multiuser Hybrid Satellite-Terrestrial Relay Network

In this letter, we study the average symbol error rate (ASER) performance in a multiuser hybrid satellite-terrestrial relay network (HSTRN) with opportunistic scheduling. The signal broadcast from the satellite is amplified by relay and forwarded to the terrestrial user that has the maximum instantaneous signal-to-noise ratio (SNR) in its channel. Specifically, both the approximate analytical formula and asymptotic expression at high SNRs of the ASER are derived for the fixed gain relaying as well as the variable gain relaying. Monte Carlo simulation are provided to verify our analysis and indicate the performance disparity between the two kinds of relaying protocols.

Aerial Base Station Assisted Cellular Communication: Performance and Trade-Off

The use of Aerial Base Stations (ABSs) has received a great deal of attention in academia and industry as a means to support the cellular communication traffic growth. In this article, we focus on obtaining the optimal altitude of an ABS using two criteria - maximum cell coverage area and minimum Symbol Error Rate (SER). Our study is done by using a probabilistic air-to-ground channel model, developed for low altitude aerial platforms via simulations on a commercial ray tracing software, for different scenarios like Urban High Rise, Urban, and Suburban. The probability distributions of the received power of the ground users and of the power delay profile at optimal ABS altitude are provided as a function of the size of the cell area. For the SER analysis, we present a system model based on Generalized Frequency Division Multiplexing (GFDM), in a time-frequency grid that is compatible with Long Term Evolution, by implementing parameters for low latency communication at the physical layer. The impact of “Better than Nyquist” pulses on the GFDM system is evaluated in terms of SER performance. From the presented results, a significant improvement is demonstrated compared to the traditional Nyquist pulses.

Examination of the multiple-input multiple-output space-time block-code selective decode and forward relaying protocol over non-homogeneous fading channel conditions

With the tremendous increase in wireless user traffic, investigation on the end-to-end reliability of wireless networks in practical conditions such as non-homogeneous fading channel conditions is becoming increasingly widespread. Because they fit well to the experimental data, generalized channel fading distributions like κ–μ are well suited for modeling diverse fading channels. This paper analyzes the symbol error rate (SER) and outage probability (OP) performance of multiple-input multiple-output (MIMO) space-time block-code (STBC) selective decode and forward (S-DF) network over κ–μ fading channel conditions considering the additive white Gaussian noise (AWGN). First, the closed-form (CF) analytical expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) as well as its moment generating function (MGF) are derived. Second, the OP performance is then investigated for various values of the channel fading parameter and SNR regimes. The simulation findings show an increase in SER performance with an improved line-of-sight (LOS) component. Furthermore, the results show that the S-DF relaying systems can function properly even when there is no fading or LOS component. The OP has been increasing with the increase in the value of μ and κ. In medium and high SNR regimes, simulation results exactly match with analytical results.

Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors

Recently, a combination of unmanned aerial vehicles (UAVs) and free-space optics (FSO) has been investigated as a potential method for high data-rate front-haul communication links. The aim of this work was to address the performance of UAV-to-ground station-based FSO communications in terms of the symbol error rate (SER). The system proposes utilizing subcarrier intensity modulation and an avalanche photo-diode (APD) to combat the joint effects of atmospheric turbulence conditions and pointing error due to the UAV’s fluctuations. In the proposed system model, the FSO transmitter (Tx) is mounted on the UAV flying over the monitoring area, whereas the FSO receiver (Rx) is placed on either the ground or top of a high building. Unlike previous works related to this topic, we considered combined channel parameters that affect the system performance such as transmitted power, link loss, various atmospheric turbulence conditions, pointing error loss, and the total noise at the APD receiver. Numerical results have shown that, for the best system SER performance, the value of an average APD gain at the Rx can be selected, varying from 18 to 30, whereas the equivalent beam waist radius at the Tx should be in a range from 2 to 2.2 cm in order to decrease the effects from the UAV’s fluctuations.

Multisegment Mapping Network for Massive MIMO Detection

The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.

Multidimensional Constellation Design for Spatial Modulated SCMA Systems

In this paper, multidimensional constellation designs of spatial modulated sparse code multiple access (SM-SCMA) systems with arbitrary number of transmit antennas are investigated to improve the system performance. Firstly, the symbol error rate (SER) performance of single-user and multi-user systems under flat independent Rayleigh fading channels is analyzed. It is shown that except for the product distance of the sub-constellation of each antenna, the product of the norm of symbols on each-dimension can affect single-user performance. Then, based on the performance analysis, a symmetric multidimensional constellation (SMC) and a golden-angle-modulation-based constellation (GAMC) are proposed for the general case where the number of transmit antennas is an arbitrary integer, and a low-complexity SMC (LSMC) is proposed for the special case where the number of transmit antennas is a power of two. Finally, the three proposed design schemes are extended to transmit correlated scenarios. Simulation results show that for uplink systems, compared with existing schemes, SMC provides the best SER performance and has no limitation on the number of transmit antennas. When the transmitter has no spatial correlation and the number of transmit antennas is a power of two, LSMC can achieve comparable performance to SMC with reduced computational complexity. For downlink systems, GAMC can obtain a considerable performance gain (up to 3 dB) over other solutions under transmit correlated channels.

Multidimensional Codebook Design Using Deep Learning Techniques for Rayleigh Fading Channels

A new approach based on deep learning techniques for multidimensional codebook (MDC) design over Rayleigh fading channels is proposed in this letter. Different from autoencoder (AE) techniques, the proposed deep neural network (DNN) can generate codebooks directly without a decoder structure. Two loss functions, one exploiting essential figures of merit (FoMs) and the other based on theoretical symbol error probability over fading channels, are introduced for the proposed DNN structure. Simulation results reveal that the resulting codebooks of the proposed approach have similar symbol error rate (SER) performance when adopting different loss functions. They have substantial SER performance gain over the codebooks learned by AEs, and reach close SER performance with codebooks conventionally designed by state-of-the-art. Moreover, the proposed approach guarantees good FoMs for the learned MDCs.

Time-Domain to Delay-Doppler Domain Conversion of OTFS Signals in Very High Mobility Scenarios

In Orthogonal Time Frequency Space (OTFS) modulation, information symbols are embedded in the delay-Doppler (DD) domain instead of the time-frequency (TF) domain. In order to ensure compatibility with existing OFDM systems, most prior work on OTFS receivers consider a two-step conversion, where the received time-domain (TD) signal is firstly converted to a time-frequency (TF) signal (using an OFDM demodulator) followed by post-processing of this TF signal into a DD domain signal. In this paper, we show that the spectral efficiency (SE) performance of a two-step conversion based receiver degrades in very high mobility scenarios where the Doppler shift is a significant fraction of the communication bandwidth (e.g., control and non-payload communication (CNPC) in Unmanned Aircraft Systems (UAS)). We therefore consider an alternate conversion, where the received TD signal is directly converted to the DD domain. The resulting received DD domain signal is shown to be not the same as that obtained in the two-step conversion considered in prior works. The alternate conversion does not require an OFDM demodulator and is shown to have lower complexity than the two-step conversion. Simulations reveal that even in very high mobility scenarios, the SE and symbol error rate performance of the alternate conversion is invariant of Doppler shift and is significantly better than the performance achieved with two-step conversion (which degrades with increasing Doppler shift).

Performance analysis and optimal detector of wireless optical space-time shift keying over the Gamma-Gamma turbulence channel.

In this paper, an optical space-time shift-keying (OSTSK) scheme based on the space-time dispersion matrix block of orthogonal design and laser selection technology is proposed to improve the transmission bits per symbol (TBS) and bit error rate (BER) performance of the traditional optical space shift keying (OSSK). The average BER of OSTSK is investigated over the Gamma-Gamma channel and simulated using the Monte Carlo method. The results show that the proposed scheme has the highest TBS and best BER performance compared with OSSK and optical generalized space shift keying. In addition, we propose a threshold judgment orthogonal matching pursuit (TJ-OMP)-decoding algorithm near-optimal detector for the OSTSK scheme to reduce the computational complexity of the system. For the OSTSK system with Nt=4 and Nr=4, when the reasonable threshold is selected as θ=1.5, the computational complexity of OSTSK-(TJ-OMP) is 74.3% lower than that of the OSTSK-maximum likelihood sequence detection (OSTSK-MLSD), while the signal-to-noise ratio of the OSTSK-(TJ-OMP) decreases by about 1.5 dB at BER=1×10-3.

SER performance of MIMO full-duplex relay system with channel estimation errors and transceivers hardware impairments

In practice, traditional wireless communication systems are often affected by various negative factors such as channel estimation errors (CEE) and transceiver hardware impairments (THI). For in-band full-duplex (FD) communication systems, besides CEE and THI impacts, imperfect self-interference cancellation will significantly degrade the system performance because of residual self-interference (RSI). This paper aims to investigate the performance of a multiple-input multiple-output (MIMO) FD relay (FDR) system in realistic scenarios where the impacts of CEE, THI, and RSI are taken into account. We mathematically derive the exact closed-form expression of symbol error rate (SER) of the considered MIMO-FDR system and validate the derived expression by Monte-Carlo simulations. Numerical results indicate that the impacts of three imperfect factors (CEE, THI, and RSI) are remarkable. Specifically, with the considered values of CEE, THI, and RSI, the impact of CEE on the SER is most substantial. Additionally, compared with the ideal system (perfect channel estimations, transceiver hardware, and self-interference cancellation), the SER of the considered system is significantly higher and goes to the error floor fast. Therefore, it is crucial to apply all solutions for reducing the CEE, THI, and RSI in the considered MIMO-FDR system.