Non-orthogonal Signals Research Articles

Intelligent non-cooperative optical networks: Leveraging scattering neural networks with small training data

Artificial intelligence (AI) is enabling intelligent communications where learning based signal classification simplifies optical network signal allocation and shifts signal processing pressure to each network edge. This work proposes a non-orthogonal signal waveform framework that leverages its unique spectral compression characteristic as a user address for efficiently forwarding messages to target users. The primary focus of this work lies in the physical layer intelligent receiver design, which can automatically identify different received signal formats without preamble notification in a non-cooperative communication approach. Traditional signal classification methods, such as convolutional neural network (CNN), rely on extensive training, resulting in a heavy dependency on large training datasets. To overcome this limitation, this work designs a specific two-layer scattering neural network that can accurately separate signals even when the training data is limited, leading to reduced training complexity. Its performance remains robust in diverse transmission conditions. Furthermore, the scattering neural network is interpretable because features are extracted based on deterministic wavelet filters rather than training based filters.

FTN-Based Non-Orthogonal Signal Detection Technique With Machine Learning in Quasi-Static Multipath Channel

FTN-Based Non-Orthogonal Signal Detection Technique With Machine Learning in Quasi-Static Multipath Channel

Enhancing signal quality in dense wavelength division multiplexed systems by eliminating crosstalk with cascaded optical bandpass filters

Non-orthogonal wavelength division multiplexing (WDM) channels are crucial in achieving significantly higher capacity and spectral efficiency than conventional schemes. Additionally, conventional schemes face challenges in electrical processing of wide-bandwidth signals. To suppress crosstalk in non-orthogonal WDM channels, the use of cascaded bandpass filters with suitable transmissivity and bandwidth is investigated for effective channel separation of large-capacity and multiplexed optical signals. This study employs the bandwidth ratio as a parameter, which is defined as the ratio of the channel spacing to the signal bandwidth, to quantify the extent of signal overlapping, and uses optical bandpass filters to filter non-orthogonal signals with a bandwidth ratio ranging from 1.0 to 0.75. The non-overlapping portion is set as the transmission bandwidth of the filter, and the crosstalk is maximally reduced with two-stage filters using Nyquist, super Gaussian, and Butterworth shaped filters. According to the analysis, the combination of Gaussian N = 1 and Gaussian N = 5 exhibits the lowest error vector magnitude (EVM) performance within a bandwidth ratio of 0.8 to 1.0. Simulations showed that a two-stage OBPF with a combination of Gaussian N = 1 and Gaussian N = 5 provides the best EVM performance at a bandwidth ratio between 0.85 to 1.0 within the combination of filter shapes and their orders we used. Additionally, the study includes an analytical investigation of the impact of amplified-spontaneous-emission noise associated with the Erbium-doped fiber amplifier (EDFA) and frequency resolution tolerance of the LCOS filter. The results demonstrate that cascaded EDFAs can be achieved down to OSNR of 10 dB at a minimum resolution of 4 GHz. Finally, the optimal filter shape is analyzed for different bandwidth ratios. The investigation shows that the optimal filter shape is independent of the function used and remains consistent across all bandwidth ratios. The tolerance of the filter is primarily affected by the −0.5 dB transmittance bandwidth between subchannels, which varies depending on the bandwidth ratio. Furthermore, a high tolerance is observed in the −20 dB transmittance bandwidth.

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals.

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.

Energy-Efficient Multiuser Localization in the RIS-Assisted IoT Networks

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

Empowering secure transmission for downlink of multiple access system relying non-orthogonal signal multiplexing

The growth of internet-of-things (IoT) inspired use cases in different run of the mill environments such as cities, industries, healthcare, agriculture, and transportation, has led to a greater desire for safer IoT data gathering and storage. However, securing IoT is challenging due to form-factor, complexity, energy, and connectivity limitations. Conventional coding-based security techniques are unsuitable for ultra-reliable low-latency and energy-efficient communication in IoT. Numerous research studies on physical layer security (PLS) techniques for fifth generation (5G) have emerged recently, but not all of the solutions can be used in IoT networks due to complexity limitations. Non-orthogonal multiple access (NOMA) is billed as a possible technology to solve connectivity and latency requirements in IoT. In this study, we exploit the power allocation characteristics of NOMA to enhance security in a downlink deviceto-device (D2D) decode and forward (DF) IoT network infiltrated by an eavesdropper. Our performance metric of choice is the secrecy outage probability (SOP). We formulate exact SOP results for different users. Simulation results demonstrate the positive impact of NOMA on SOP in a D2D IoT-NOMA network.

Evolutoin of algorithms for separation of optimal two mutually unorthogonal signals of binary phase modulation

A review of the results of the development of the methodology for the synthesis of the separation-demodulation algorithms of two mutually non-orthogonal binary phase modulation signals in the length of the information clock interval, synchronous and asynchronous according to the clock points, with continuous and intermittent radiation. The evolution of algorithms for optimal separation of two mutually non-orthogonal signals of binary phase modulation has the following sequence: signals are mutually synchronous by clock points (communication channel – with constant parameters, non-seeding oscillations are represented by functions integrated with the square; the signals are mutually synchronous in clock points, the second interfering signal is intermittent; signals are mutually asynchronous in clock points; the signals are mutually asynchronous in clock points, the second interfering signal is intermittent; signals are mutually synchronous in clock points, both signals are discontinuous. At the same time, the minimum probability of an error in the estimation of the discrete information parameter of the first (useful) signal was assumed as the optimality criterion. The results of a comparative review of algorithms for the separation of two mutually non-orthogonal binary FM signals demonstrate their evolutionary nature. The identified main regularities make it possible to obtain descriptions of signal separation procedures with mismatched clock frequencies without performing intermediate signal processing procedures. The gradual process of complicating synthesis tasks led to the discovery of gradual regularities in the structures of the separation-demodulation algorithms of two binary FM signals, which provides an opportunity to formulate directions for further research, which should be based on the provisions of the theory of multi-user detection. Namely, the revealed regularities are the basis for conducting further research in order to solve similar problems with the gradual further complication of the initial conditions. Priority consideration in the future will be given to tasks that have theoretical interest and practical value: when both signals are characterized by non-stationary intermittent emission modes and are asynchronous in clock points; when they are characterized by packet transmission mode; when band-efficient types of modulation are used (MSK, GMSK – first of all).

Piecewise Iterative Extrapolation Method for Bandlimited Signal

In some high spectrally efficient communication structures, the Gerchberg-Papoulis extrapolation algorithm can be used to reconstruct nonorthogonal signals. However, the extrapolation efficiency and accuracy degrades when the energy of known signal is small or the iterative filter bandwidth is large. Focused on these drawbacks, a piecewise iterative extrapolation method is proposed to extrapolate bandlimited signals with higher extrapolation efficiency and accuracy. In this paper, the amplitude change of the unknown part over iterations is analyzed and the feasibility of the proposed extrapolation method is discussed. Numerical simulation shows the accuracy superiority of proposed method with the frequency offset compared to the GP algorithm with fixed computational complexity. Besides, the feasibility of the proposed method is verified in fractional Fourier domain with performance advantages.

Medium access control protocol design for wireless communications and networks review

<p><span>Medium access control (MAC) protocol design plays a crucial role to increase the performance of wireless communications and networks. The channel access mechanism is provided by MAC layer to share the medium by multiple stations. Different types of wireless networks have different design requirements such as throughput, delay, power consumption, fairness, reliability, and network density, therefore, MAC protocol for these networks must satisfy their requirements. In this work, we proposed two multiplexing methods for modern wireless networks: Massive multiple-input-multiple-output (MIMO) and power domain non-orthogonal multiple access (PD-NOMA). The first research method namely Massive MIMO uses a massive number of antenna elements to improve both spectral efficiency and energy efficiency. On the other hand, the second research method (PD-NOMA) allows multiple non-orthogonal signals to share the same orthogonal resources by allocating different power level for each station. PD-NOMA has a better spectral efficiency over the orthogonal multiple access methods. A review of previous works regarding the MAC design for different wireless networks is classified based on different categories. The main contribution of this research work is to show the importance of the MAC design with added optimal functionalities to improve the spectral and energy efficiencies of the wireless networks.</span></p>

Exact Analytical H-BER for Ad Hoc XOR H-Map Detector for Two Differentially Modulated BPSK Sources in H-MAC Channel

In the article, we present an ad hoc (AH) detector for two differentially encoded BPSK sources in the Hierarchical MAC (H-MAC), i.e., for the case when the receiver sees the superposition of non-orthogonal signals from individual sources. (Prefix “H-” means Hierarchical, it emphasizes that the entity is related to the many-to-one principle.) The AH detector decodes the XOR H-map of the two BPSK streams—in other words, it decides whether the transmitted symbols from the two sources are the same or opposite. The BER of the detection in H-MAC is denoted as H-BER. The H-BER is compared with the other two differential detectors, with the coherent (Coh) detector, and with an approximate coherent (ApC) detector. The exact analytical H-BER formula is derived for the ad hoc and coherent detectors. The proposed ad hoc detector is very simple for evaluation, does not require the estimation of subchannel phases, does not depend on noise variance, and it is uniformly only roughly 3.5 dB worse than the coherent one.

Covert Communication in Intelligent Reflecting Surface-Assisted NOMA Systems: Design, Analysis, and Optimization

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

Non-orthogonal signal representation using redundant dictionaries gradually gained popularity over the last decades. Sparse methods find major application in signal denoising, audio declipping, time-frequency analysis, and classification, to name a few. This paper is inspired by the exceptional results of sparse representation classification originally suggested for face recognition. We compare the method to other common classifiers using simulated as well as real datasets. In the latter the proposed method is tested with real pressure data from a bed equipped with a matrix of 30 × 11 pressure sensors. Here the method outperforms standard classification methods (surpassing 91 % accuracy) without need of parameter selection or special user's skills. Furthermore it offers a means of dealing with occlusions, whose results are presented as well.

Deep intelligent spectral labelling and receiver signal distribution for optical links.

A unique automatic receiver signal distribution strategy is proposed for private optical networks based on the concept of non-orthogonality. A non-orthogonal signal waveform can compress the spectral bandwidth, which not only fits a signal in a bandwidth limited scenario, but also enables the compression ratio information for labelling. Depending on a unique value of spectral compression, an end user destination can be correlated. A network edge node will rely on deep learning to intelligently identify each raw signal and forward it to corresponding end users with no sophisticated digital signal pre-processing. In this case, signal identification and distribution are faster while computationally intensive signal compensation and detection will be shifted to each end user since the receiver is highly dynamic and user-defined in private optical networks. An intelligent signal classifier will be trained considering various fiber transmission factors such as transmission distance, training dataset size and launch power. At the end, a universal classifier is obtained, which can be used to identify signals in a system for any fiber transmission distance and launch power.

Multi-cell, Multi-user, and Multi-carrier Secure Communication Using Non-Orthogonal Signals’ Superposition with Dual-Transmission for IoT in 6G and Beyond

Considering the advancements of the internet of things (IoT) in 6G and beyond communications, data transmission security in IoT devices has received extensive interest because of their significant features, such as low computational complexity, led by low power requirements. In such devices, the conventional cryptographic techniques may fail to provide secure communication. To fight this drawback, physical layer security (PLS) has remarkable potential to provide security solutions suitable for such applications. In this work, a highly effective PLS technique is proposed for providing secure communication against external and internal eavesdroppers in a downlink multi-cell, multi-user, and multi-carrier IoT communication system. In our proposed system, we considered two base stations, where each base station uses a single radio frequency (RF) chain to link two antennas that are used for the transmission of data. Further, we transmit the data in two rounds, and each round of transmission occurs through a single active antenna of each base station. A different antenna is used for each round of transmission to communicate with two single antenna IoT devices/users in the presence of a passive eavesdropper. In the proposed algorithm, frequency selective channel-based pre-coder matrices and the dual transmission approach are jointly employed. The dual-transmission is performed simultaneously from two base stations to provide security against internal and external eavesdroppers. The proposed system is suitable for IoT-based applications. Also, the potential capabilities of our proposed algorithm are proved by extensive mathematical and simulation analysis.

Method for the Blind Estimation of Power Coefficients in PD-NOMA Systems

A method of power domain non-orthogonal multiple access with user channel multiplexing is a promising approach for organizing multichannel communication in modern digital wireless systems. Generation of a non-orthogonal group signal involves the superposition of user signals with different weight power coefficients in a single frequency-time resource segment. Decoding such a signal at the receiver requires the information about the weights used in multiplexing. This information is transmitted in control channels and provides additional load on the data network, which in turn reduces its effectiveness. A blind estimation method of power coefficients is proposed to solve this issue. A simulation model has been developed to study proposed method. Simulation resulted in the dependence of the blind estimation of power coefficients error on the signal-to-noise ratio in the transmission channel and on the signal sample length. The result of the study showed that the blind estimation of power coefficients insignificantly worsens the probability of a bit error when decoding a group non-orthogonal signal. Despite this, the proposed blind estimation method can be considered in an attempt to optimize control channel traffic.

Methodology and Results of Synthesis and Analysis of Potential Resilience for Noise Immunity Compensator of an Asynchronous Intermittent Interference Similar to a Useful Phase-Manipulated Signal

In telecommunication radio systems with random multiple-access (RMA), user signals are characterized by a random intermittent radiation mode and the occurrence of their collisions in the propagation medium, i.e. conflicts at the physical level. Practical interest belongs to the situations when the useful and interfering (intermittent) signals are asynchronous at clock points. It should also be noted that when there are intermittent mutually non-orthogonal signals along the length of the information error more than two, the detection-separation algorithms that are optimal in terms of the minimum probability of error in estimating the discrete parameter of the useful signal are too complex. Therefore, the simplest case is investigated here, when the interfering signal is the only one. An algorithm for demodulation of a binary phase-manipulated signal is observed, which is observed at the background of a similar asynchronous clock noise, which is characterized by a random intermittent radiation mode. The minimum probability of error in the discrete information parameter estimation of the useful signal is chosen as the criterion of optimum for the synthesis. It is also assumed that all non-information parameters of the useful signal and similar interference are precisely known. The distribution medium is considered to be stationary in time. These initial data for the synthesis allow obtaining in the analysis the potential limits of the noise immunity of the digital signal demodulation, which is observed at the background of a similar intermittent noise. The result is a «framework» of the demodulation-separation procedure, which should be subsequently supplemented with nodes (blocks) of continuous parameters estimation that are not informational - frequencies, initial phases, amplitudes, shapes of bending, clock points, etc. The algorithm for demodulating a digital signal under the influence of such an asynchronous intermittent interference turns out to be about twice as complicated in comparison with the previously known one, when the signal and the interference at the clock points were assumed to be synchronous. A characteristic feature of the obtained compensation algorithm is the absence of feedback - the compensation procedure is performed forward, at the outputs of the signal correlators and interference. The result is generalized to the case when the clock frequencies of the signal and interference differ by an arbitrary value. A simplified approximation of the obtained algorithm is proposed.

Low-Resolution Limited-Feedback NOMA for mmWave Communications

The spectrum-efficient millimeter-wave (mmWave) communications has recently attracted much attention as a viable solution to spectrum crunch problem. In this work, we propose a novel non-orthogonal multiple access (NOMA) framework, which makes use of the directional propagation characteristics of mmWave communications so as to improve the spectral efficiency through non-orthogonal signaling. In particular, we consider one-bit quantized angle information as a limited yet effective feedback scheme describing the channel quality of user equipment (UE) in mmWave bands. The UE pairs for NOMA transmission are then established using not only the one-bit distance feedback as a classical approach, but also the one-bit angle feedback. The proposed strategy is therefore referred to as two-bit NOMA. We also propose a novel hybrid strategy, called combined NOMA, for the circumstances with no UE pair through two-bit NOMA. Whenever no UE pair is available through any NOMA strategy, we resort to single user transmission (SUT) with proper UE selection schemes. The hybrid sum-rate performance is also analyzed thoroughly with the respective outage and rate expressions. The numerical results verify that the proposed strategy outperforms one-bit NOMA schemes with either angle- or distance-only feedback.

Zero Padding or Cyclic Prefix: Evaluation for Non-Orthogonal Signals

The debate of using zero padding (ZP) instead of a cyclic prefix (CP) for enhancing channel estimation and equalization performance is a recurring topic. This is particularly true for orthogonal signals, such as orthogonal frequency division multiplexing (OFDM). Yet, there are far fewer studies evaluating the impact of ZP and CP in non-orthogonal systems. Such systems have the added complexity of self-induced interference rendering channel estimation and equalization more challenging. For this reason, this work proposes a new channel estimation and equalization technique for non-orthogonal systems, which combines ZP with an orthogonal demodulator. Results show that the multipath components that appear in the ZP part can be used to enhance performance when compared to the CP approach.

Orthogonal and Non-Orthogonal Signal Representations Using New Transformation Matrices Having NPM Structure

In this paper, we introduce two types of real-valued sums known as Complex Conjugate Pair Sums (CCPSs) denoted as CCPS$^{(1)}$ and CCPS$^{(2)}$, and discuss a few of their properties. Using each type of CCPSs and their circular shifts, we construct two non-orthogonal Nested Periodic Matrices (NPMs). As NPMs are non-singular, this introduces two non-orthogonal transforms known as Complex Conjugate Periodic Transforms (CCPTs) denoted as CCPT$^{(1)}$ and CCPT$^{(2)}$. We propose another NPM, which uses both types of CCPSs such that its columns are mutually orthogonal, this transform is known as Orthogonal CCPT (OCCPT). After a brief study of a few OCCPT properties like periodicity, circular shift, etc., we present two different interpretations of it. Further, we propose a Decimation-In-Time (DIT) based fast computation algorithm for OCCPT (termed as FOCCPT), whenever the length of the signal is equal to $2^v,\ v{\in} \mathbb{N}$. The proposed sums and transforms are inspired by Ramanujan sums and Ramanujan Period Transform (RPT). Finally, we show that the period (both divisor and non-divisor) and frequency information of a signal can be estimated using the proposed transforms with a significant reduction in the computational complexity over Discrete Fourier Transform (DFT).