Transform Domain Communication System Research Articles

Multiple transformation analysis for interference separation in TDCS

Various types of interference signals limit the practical application of transform domain communication systems (TDCSs) in the severe electromagnetic field, an orthogonal basis learning method of transformation analysis (OBL-TA) is proposed to effectively address the problem of obtaining an optimal transform domain based on sparse representation. Then, the sparse availability is utilized to obtain the optimal transformation analysis by the iterative methods, which yields the sparse representation for transform domain (SRTD) in unrestricted form. In addition, the iterative version of SRTD (I-SRTD) in unrestricted form is obtained by decomposing the SRTD problem into three sub-problems and each sub-problem is iteratively solved by learning the best orthogonal basis. Furthermore, orthogonal basis learning via cost function minimization process is conducted by stochastic descent, which is assured to converge to a local minimum at least. Finally, the optimal transformation analysis is developed by the effectiveness of different transform domains according to the accuracy of the sparse representation and an optimal transformation analysis separately (OPTAS) is applied to the synthesized signal forms with conic alternatives, dualization, and smoothing. Simulation results demonstrate that the superiorities of the proposed methods achieve the optimal recovery and separation more rapidly and accurately than conventional methods.

Transform-Domain-Based Cognitive Radio Networks for Harsh Interference Environments

In harsh interference environments, there are all kinds of electromagnetic interference to disturb the communication links. In this article, by integrating transform domain communication system (TDCS) into cognitive radio (CR), transform-domain-based cognitive radio (TD-CR) is proposed to flexibly adopt TD-based spread spectrum technology to generate an adaptive anti-interference modulation waveform over the idle spectrum, which will improve both the anti-interference performance and spectrum efficiency of CR in the harsh interference environments. First, the transmitter and receiver of the TD-CR system are designed, where the key technologies, including spectrum sensing, spectrum magnitude shape, random phase mapping, fundamental modulation waveform, and signal detection, are described. Second, novel interleave-division multiple access and clustering multiple access for the TD-CR are proposed to improve the multiuser spectrum access ability. Thirdy, artificial-intelligence-enabled TD-CR is proposed to achieve intelligent spectrum sensing and optimal waveform transform by a cognitive engine. Then simulation results are provided to verify the anti-interference performance of the TD-CR. Finally, some future research challenges are discussed.

Efficient Non-Uniform Pilot Design for TDCS.

The Internet of Things (IoT) leads the era of interconnection, where numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, wireless communication technologies have to overcome available spectrum shortage and complex fading channels. The transform domain communication system (TDCS) is a cognitive anti-interference communication system with a low probability of detection and dynamic spectrum sensing and accessing. However, the non-continuous and asymmetric spectrum brings new challenges to the traditional TDCS block-type pilot, which uses a series of discrete symbols in the time domain as pilots. Low efficiency and poor adaptability in fast-varying channels are the main drawbacks for the block-type pilot in TDCS. In this study, a frequency domain non-uniform pilot design method was proposed with intersecting, skewing, and edging of three typical non-uniform pilots. Some numerical examples are also presented with multipath model COST207RAx4 to verify the proposed methods in the bit error ratio and the mean square error. Compared with traditional block-type pilot, the proposed method can adapt to the fast-varying channels, as well as the non-continuous and asymmetric spectrum conditions with much higher efficiency.

Interference classification and identification of TDCS based on improved convolutional neural network

A classification and recognition algorithm based on short-time Fourier transform and convolutional neural network (STFT-CNN) is proposed to solve the common interference signal classification and recognition problem in transform domain communication systems. In this algorithm, the time-spectrum diagram of interference signals obtained by short-time Fourier transform is input into the vggnet-16 network model improved according to STFT characteristics for feature learning and training, and the classification and recognition of signals are completed. Simulation results show that the proposed algorithm for comprehensive recognition rate reached 97.7%, 6 kinds of jamming signal in low SNR circumstance still can reach more than 93% recognition rate, compared with the traditional algorithm, this method not only improves the classification recognition rate of single interference, but also improves the recognition of mixed interference ability, has the ability to resist low signal-to-noise ratio, makes the transform domain communication system can choose transform domain for anti-interference, provides theoretical basis and support for the application of convolutional neural network in anti-interference of communication system in transform domain.

Design of orthogonal phase encoding for basis function based on genetic algorithm

A new basis function generation algorithm based on genetic algorithm is proposed to overcome the drawbacks of the traditional basis function phase encoding algorithm in transform domain communication system (TDCS). Firstly, by analysing the requirements and the characteristics of basis function in TDCS, the evaluation criterion of basis function is presented, and the corresponding genetic algorithm is designed according to the criterion. Then, the algorithm is used to generate the orthogonal basis function set. The proposed algorithm not only improves the correlation performance and randomness of basis function, but also improves the system multiple access performance and security. Furthermore, the length and quantity of the basis functions can be flexibly selected and the system's applicability is improved. By analysing the simulation results, the feasibility and validity of the algorithm are verified.

Design of orthogonal phase encoding for basis function based on genetic algorithm

A new basis function generation algorithm based on genetic algorithm is proposed to overcome the drawbacks of the traditional basis function phase encoding algorithm in transform domain communication system (TDCS). Firstly, by analysing the requirements and the characteristics of basis function in TDCS, the evaluation criterion of basis function is presented, and the corresponding genetic algorithm is designed according to the criterion. Then, the algorithm is used to generate the orthogonal basis function set. The proposed algorithm not only improves the correlation performance and randomness of basis function, but also improves the system multiple access performance and security. Furthermore, the length and quantity of the basis functions can be flexibly selected and the system's applicability is improved. By analysing the simulation results, the feasibility and validity of the algorithm are verified.

Application Analysis of LS Channel Estimation for Transform Domain Communication System

To reduce the influence of wireless transmission channel on transform domain communication system (TDCS) and improve the channel estimation accuracy, a system model of Least Square (LS) channel estimation on TDCS is established. In this paper, the influence of the unavailable frequency existing in basic function is analysed for the LS channel estimation, then the expression of mean square error (MSE) is derived. After that, the frequency pilot insertion pattern and the interpolation algorithm suitable for TDCS is proposed. The simulation results verify the paper’s theoretical analysis, and the effectiveness of the proposed algorithm is proved by the comparison of the MSE and BER performance.

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

In this paper, we propose a computationally efficient multipath matching pursuit (MMP) channel estimation algorithm for underwater acoustic (UWA) transform domain communication systems (TDCSs). The algorithm, referred to as the MMP-DCD-CV algorithm, is based on the dichotomous coordinate descent (DCD) iterations and cross validation (CV). The MMP-DCD-CV sparse channel estimator in each iteration searches for multiple promising path candidates most relevant to a residual vector and chooses the best candidate. The DCD iterations are used to solve the corresponding least squares problem with low complexity and numerical stability. The CV provides a stopping criterion of the algorithm without a priori information on the channel sparsity and noise level and examines whether the algorithm overfits its data, thus improving the estimation accuracy. The performance of the proposed algorithm is evaluated under simulated sparse UWA channels. The numerical results show that the algorithm achieves better performance than the original MMP algorithm, has lower complexity, and does not require prior knowledge on the channel sparsity and noise level. We also propose an UWA TDCS with sparse channel estimation based on the proposed MMP-DCD-CV algorithm. The proposed UWA communication system is tested by the Waymark simulator, providing the virtual signal transmission in the UWA channel, with a measured Sound Speed Profile and bathymetry. Numerical results demonstrate that the UWA TDCS with the proposed sparse channel estimator offers considerable improvement in system performance compared to other TDCS schemes.

PAPR Reduction of TDCS Based on Chirp Noise Sequence

This letter proposes a transform domain communication system (TDCS) based on chirp noise sequence to deal with its high peak to average power ratio (PAPR). The optimal chirp noise sequence is obtained by the selective mapping to improve PAPR reduction of the proposed TDCS waveform with low complexity. Compared with other PAPR reduction algorithms, such as magnitude weighting selection and complementary peak reducing signal, the proposed TDCS based on optimal chirp noise sequence has about 1.5 dB PAPR reduction gain without extra bit error rate loss.

Modified WFRFT-based Transform Domain Communication System Incorporating with Spectrum Mismatching

Modified WFRFT-based Transform Domain Communication System Incorporating with Spectrum Mismatching

On Narrowband Interference Suppression in TDCS with WFRFT Preprocessing

We propose a modified transform domain communication system (TDCS) structure with weighted fractional Fourier transform (WFRFT) preprocessing over the channel with additive white Gaussian noise (AWGN) and narrowband interference (NBI). The majority of NBI can be removed by adding zeros to TDCS’s basic function (BF) amplitude vector at these subcarriers where strong NBI exists, and the corresponding residual NBI can be further greatly eliminated in time-frequency domain through WFRFT processing. To achieve the best bit error rate (BER) performance, we defineγas the normalised cross relationship coefficient between TDCS’s BF vector and residual NBI vector and prove that the corresponding WFRFTαoptcan be obtained by minimising the magnitude ofγ. Numerical simulations are conducted to evaluate the proposed system’s performance with regard to NBI suppression.

Threshold Decision Algorithm Based On The Maximum Interclass Variance For TDCS

Transform domain communication system (TDCS) is a new type of wireless communication system, who can take the initiative to avoid interference. In this paper, Fractional Fourier Transforms (FRFT) is introduced into Transform Domain Communication Systems (FRFT-TDCS), which can effectively target linear frequency modulation (LFM). But the multicomponent linear frequency modulation (MLFM) is difficult to effectively eliminate, when using traditional threshold decision algorithm of TDCS. A novel threshold decision algorithm for FRFT-TDCS is proposed in this paper. Because the order of FRFT has no effect on noise, the threshold decision algorithm by using the idea of maximum interclass variance means minimum misclassification probability. Simulation results show that compared with the traditional hard threshold decision algorithm, the proposed algorithm can effectively eliminate MLFM interference and negative influence of the order of FRFT. Whether bipolar modulation or cyclic shift keying(CSK) modulation, the bit error rate(BER) of this threshold decision algorithm is always better than the traditional threshold decision algorithm; With the increase of signal to noise ratio(SNR), the BER performance of the two threshold decision algorithms increase gradually.

Embedding WFRFT Signals Into TDCS for Secure Communications

This paper presents a novel combinatory strategy to embed weighted-type fractional Fourier transform (WFRFT) precoding signals into transform domain communication system (TDCS). The original data can be divided into two parts. One part is modulated with the cyclic code shift keying in the traditional TDCS. The other part is first disguised as other higher order baseband modulations MPSK through WFRFT proceding process, then will further be embedded into the traditional TDCS system. A double-random scheme is designed to dynamically generate the pseudo phase for WFRFT rotation and TDCS basic function’s phase. Theoretical analyses show that the higher spectral efficiency can be obtained while causing little effect on its original communication quality. The final simulations demonstrate that the constellation precoding method can help disguise the original signals into other higher order baseband PSK modulations with a controllable Gaussian-like noise. Besides, owing to the dynamically double-random phase generation scheme, the proposed combinatory WFRFT-TDCS system can gain better anti-eavesdropping capacity while maintaining its own communications’ quality with regards to bit error rate performances.

A Multichannel Cognitive Radio System Design and Its Performance Optimization

Multichannel cognitive radio (CR) has been proposed to improve system throughput by selecting multiple idle subchannels for transmissions. However, these idle subchannels are often non-continuous, which cannot be utilized by the traditional access models, such as orthogonal frequency division multiplex and multicarrier code division multiple access. In this paper, a CR system based on transform domain communication system has been designed to access the non-continuous spectrum, which detects the spectrum status by energy detection and marks the spectrum availability as a spectrum marker vector. A basis carrier generated from the spectrum marker vector may concentrate the power on the idle subchannels. The bit error rate of the CR system is analyzed both in the cases of spectrum inconsistency and multiple access. The transmission data is modulated on the basis carrier using binary modulation and cyclic code shift keying modulation. A system optimization unit is designed to improve the system throughput by jointly optimizing sensing threshold and transmission power of each subchannel. The simulation results have shown that the proposed CR system may outperform the spread spectrum system and achieve larger throughput through performance optimization.

Practical Implementation of Multi-User Transform Domain Communication System for Control Channels in Cloud-Based Cognitive Radio Networks

Evolving from cognitive radio networks (CRNs), the concept has developed into a new paradigm of cloud and big data-based next-generation CRNs due to huge amount of data processing, complicated spectrum resource scheduling, and real-time information exchange. In cloud-based CRNs, control channels are needed for CR nodes to perform certain handshakings for network self-organization, spectrum sensing, network coordination, and flexible data connections. This paper investigates a transmission scheme for control channel (CC) in cloud-based CRNs, which is over several noncontiguous spectral holes. Transform domain communication system (TDCS)-based transmission scheme with spectrally-constrained sequence design is presented for CC. A practical testbed design for TDCS-based CC with multiple National Instruments PXIe devices and six universal software defined radio reconfigurable input/output devices is presented. Details of system design as well as main implementation challenges are described. Bit-error rate of the system is validated through both theoretical analysis and simulation results under realistic channel conditions.

On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis

Towards the era of mobile Internet and the Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of the increasing shortage of spectrum resources and massive multiple access. The transform-domain communication system (TDCS) is considered as an alternative multiple access system, where 5G and mobile IoT are mainly focused. However, previous studies about TDCS are under the assumption that the transceiver has the global spectrum information, without the consideration of spectrum sensing mismatch (SSM). In this paper, we present the deterministic analysis of TDCS systems under arbitrary given spectrum sensing scenarios, especially the influence of the SSM pattern to the signal to noise ratio (SNR) performance. Simulation results show that arbitrary SSM pattern can lead to inferior bit error rate (BER) performance.

Complementary peak reducing signals for TDCS PAPR reduction

Transform domain communication systems (TDCSs) are cognitive anti-interference multi-carrier communication systems with dynamic spectrum access. The inherent high peak-to-average power ratio (PAPR) of TDCS reduces the efficiency of the power amplifier. Adaptive waveform generation of the TDCS also causes the PAPR to vary according to the spectral conditions on hand. In this study, a complementary peak reducing signal (CPRS) method is proposed and analysed. It uses all unoccupied frequency bins to transmit data and uses all interfered frequency bins to generate CPRSs. Every data signal and its corresponding CPRS are orthogonal and complementary, so as to fully occupy all frequency bins. Therefore, the PAPR reduction of the composite signal with all frequency bins is considered. Once the optimal pseudo-random phase sequence is determined, the sequence can adapt to all spectral conditions without side information, and the computational complexity for diverse spectral conditions is greatly reduced. Moreover, the orthogonality between the data signal and its CPRS in the frequency domain eliminates distortions and spectral spreading. As a component of the transmitting signal, CPRS may cause bit error rate (BER) loss. This study also proposes a signal power adjustment mechanism to achieve a compromise between PAPR reduction and BER loss.

Magnitude weighting selection: a method for peak‐to‐average power ratio reduction in transform domain communication system

One of major drawbacks of transform domain communication system (TDCS) is the high peak-to-average power ratio (PAPR) of the transmitted signal. A novel method named magnitude weighting selection (MWS) for PAPR reduction in TDCS is proposed. The method is based on multiplying used subcarriers by magnitude weights which are determined by the modified backtracking search optimisation algorithm. The MWS algorithm can significantly reduce the computational complexity with a low PAPR. Furthermore, it guarantees the normality of transmission signals and reduces the multiple-user interference. Analytic and simulation results demonstrate that the MWS method makes a good tradeoff between PAPR reduction and bit error rate degradation in a specified magnitude weighting range.

A Method for Designing Low Peak to Average Power Cognitive Radar Waveform

In this paper, the basis function of TDCS system (Transform Domain Communication System) is used to design cognitive radar transmitter waveform. Based on the noise environment, the waveform can be adjusted adaptively. Then after clipping to reduce the PAPR (Peak to Average Power Ratio) of radar echo signal, the obtained signal is used as the next launch of the radar signal. The cognitive radar transmitter requires a nonlinear waveform to improve the efficiency of radar transmitter power amplifier, so reducing the PAPR of signal is essential. This paper presents the gray complementary sequence is applied to TDCS communication systems, and then it needs to reduce PAPR of signals by clipping. Proven, PAPR of the transmitted signal is maintained at about 3.25dB, and the signal has a low probability of intercept, high anti-interference ability and distant target detection range.

On the modulation and signalling design for a transform domain communication system

Transform domain communication system (TDCS) has been proposed to establish a communication link with a low probability of interception by synthesising an adaptive waveform containing energy only in the unused frequency bands. However, this communication system suffers from a low spectral efficiency. To improve its spectral efficiency, a unified modulation framework for a TDCS is proposed in this study to embrace the previously reported modulation schemes under one framework. The resulting spectral efficiency is higher compared to these previous schemes. Also, to combat the slow channel fading, a TDCS system using two transmit antennas and one receive antenna with the proposed modulation scheme is presented. Simulation results show that the multiple-antenna system improves the performance dramatically compared to the single antenna system under the slow fading environment.