With the increasing demands for transmission performance and security in optical access networks, this paper proposes a high-security floating probabilistic encryption method based on chain-embedded masking. The constellation points of this chained structure are distributed in a chain-like pattern across concentric circles. A core hexagon is formed by the points on the first layer around the origin. The second layer is then expanded into a star-shaped structure using external triangles, while the remaining points are fixed in the outermost ring. It effectively reduces both the average transmission power and the peak power while maintaining a minimum Euclidean distance of 1, resulting in a constellation figure of merit (CFM) value of 0.444. Based on this structure, a floating chain-embedded encryption scheme is further proposed. A Lorenz chaotic model is employed to generate sequences that dynamically perturb the positions of the outer-ring constellation points within the chain-like structure according to a "0-hold, 1-shift one step, 2-shift two steps" rule. Experimental validation was performed on a seven-core fiber transmission system. The results demonstrate that at a bit error rate of 3.8 × 10-3, the proposed Hierarchical Triangular-Distorted Hexagonal (HTDH) 16 Quadrature Amplitude Modulation (16QAM) constellation achieves a 0.48 dB improvement in receiver sensitivity compared with conventional 16QAM. The encrypted signals exhibit an additional 0.33 dB sensitivity enhancement over unencrypted signals. This indicates that the encryption mechanism not only enhances security but also synergizes with the energy concentration characteristics of the constellation, ultimately achieving co-optimization of transmission security and system sensitivity.

We propose a data center secure communication scheme that uses chaotic diversity index modulation for identity authentication, and design a neural network (NN) that balances decryption and detection for the receiver. Chaotic diversity is obtained from a randomly partitioned standard 16QAM constellation. The watermark is subsequently embedded into the symbol index by leveraging this diversity. Finally, the constructed data frames composed of symbols across different time slots and subcarriers is disrupted, and the watermark is concealed within the massive data transmission. The receiver takes the received signal and chaotic diversity as inputs for the NN to obtain the watermark, and achieves identity authentication by comparing it with the locally stored watermark. The 56.37Gb/s transmission verification was conducted on a 10 km standard single-mode fiber (SSMF), and the results showed that the scheme has high security. The total key space can reach 1090. The watermark is hidden in the index of the symbol, making it difficult for eavesdroppers to detect. The combination of chaotic diversity and NN can cause data pollution to eavesdroppers, making it difficult to steal the correct NN. The accuracy of NN detection exceeds that of the log-likelihood ratio (LLR) detector, and it can reduce the time complexity of detection by 4 orders of magnitude. The recognition accuracy of watermarks can reach 100% under high optical power, and the legitimate and illegitimate parties can be effectively distinguished, making identity disguise attacks difficult to achieve.

This paper proposes two quadrature amplitude modulation (QAM) constellation integration schemes for recon figurable intelligent surface (RIS)-enabled symbiotic backscatter systems termed Schemes I and II, where the primary and backscatter signals merge into a standard 16-QAM constellation by offsetting the amplitude-phase coupling effects of a practically measured RIS, Specifically, the primary and backscatter signals adopt: (a) a binary phase-shift keying and a symbol subset of eight symbols selected from a 16-QAM constellation for Scheme I; (b) a quadrature phase-shift keying and a symbol subset of four symbols selected from a 16-QAM constellation for Scheme II. On this basis, we design the corresponding detector and derive closed-form expressions of the symbol error rate (SER) and throughput of the primary signal, the backscatter signal, and the whole system for the underlying schemes. Our theoretical and numerical results show that the SER and throughput of Scheme II outperform those of Scheme I at low and medium transmit signal-to-noise-ratios (SNRs), and tend to be the same at high transmit SNR.

ABSTRACT Reconfigurable intelligent surface (RIS)‐assisted received spatial modulation (RIS‐RSM) has emerged as a promising technique to enhance spectral and energy efficiency in next‐generation wireless systems. However, performing accurate signal detection without channel state information (CSI) remains a critical challenge, particularly in blind detection scenarios. In this paper, we propose a novel clustering‐based blind detector named energy‐tiered structure initialization (ETSI). The proposed method exploits the amplitude heterogeneity of modulation symbols—originating from the unequal energy levels of QAM or hybrid constellations—by partitioning the signal space into multiple energy tiers. Each receive antenna is associated with a structure prototype, representing a normalized statistical channel pattern, which is initialized using the distribution of the underlying fading model (e.g., Rayleigh) rather than instantaneous CSI. During clustering, these prototypes are iteratively refined through tier‐wise averaging of normalized signal samples, thereby enforcing structural consistency and mitigating amplitude‐induced bias. After convergence, the constellation‐aligned cluster centres are reconstructed by combining the updated prototypes with their corresponding modulation amplitudes, inherently enabling the joint detection of both the modulation symbol and the RIS‐assisted spatial index. Simulation results show that ETSI achieves around 0.5–1 dB SNR gain over amplitude–phase aware clustering under 8PSK, and about 1–1.5 dB improvement under 16QAM, while outperforming the CSI‐based greedy detector (GD) across both modulations. Moreover, ETSI achieves BER performance close to that of the perfect‐CSI maximum likelihood detector, confirming its accuracy, scalability and practical feasibility for blind RIS‐RSM detection.

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple quadrature amplitude modulation (QAM) constellation streams to users equipped with multiple receive antennas. We begin by formulating a symbol-level joint design scheme aimed at collaboratively optimizing the transmit precoding and receive combining matrices. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.

We present an all-optical de-aggregation scheme that converts a single 16QAM signal into two PAM-2 and two PAM-4 channels by dual-pump phase-sensitive amplification (PSA) in a silicon-CNT/PDMS hybrid slot waveguide. The waveguide design provides a phase mismatch bandwidth from 1350 to 1850nm and achieves a high nonlinearity of 1.308×108W-1m-1 at 1550nm. We divide the 16QAM constellation into outer-ring S1 and inner-ring S2 subsets, then de-aggregate each by PSA. At a 4.2mW pump power, the PSA achieves approximately 30dB of gain. Numerical simulations and analyses of constellations, output signal-to-noise ratio (SNR), and error-vector magnitude (EVM) show that, as the input SNR varies from 8 to 32dB, the two PAM-2 channels improve EVM from 31% to 5%, while the two PAM-4 channels improve EVM from 31% to 7%. These results demonstrate the scheme's potential for flexible format conversion and de-aggregation in dynamic opticaletworks.

Index modulation, which develops the additional information using the antenna index (AI) domain, is a promising modulation technique for next wireless communications. In view of a rich radio scattering environment around transmit antennas (TAs), utilizing the indexes of channel fade realizations, media-based modulation (MBM), which develops the additional information using the channel index (CI) domain, is a recently potential channel modulation and viewed as a potential key technique for enhancing the reliability of communication systems. In this paper, to simultaneously carry the additional information including the AI and CI information bits, the integration of index modulation and MBM is investigated. Firstly of all, the application of the MBM technique to the extended space index modulation (ESIM) system, called as ESIM-MBM, is investigated to improve the spectral efficiency (SE) and error performance of the multiple-input multiple-output with index modulation (MIMO-IM) systems. Specifically, after obtaining one ESIM vector by modulating the real and imaginary parts of one mapped symbol on one or two active TAs using the selected AI vector, according to the number of active TAs, the real and imaginary parts of one mapped symbol are respectively performed Kronecker products by two selected CI vectors with two subparts of CI bits, result in the transmitted MBM vector. Secondly, in order to further extend the size of signal spaces to improve the AI information, with the combination of one or two signal points from QAM and secondary QAM constellations, a new design of enhanced signal spaces with two active TAs assisted the MBM system (ESS-TTAs-MBM) is proposed to enhance the throughput of the communication system. Furthermore, using the maximum likelihood (ML) at the receiver, the comparisons of detection complexities of the proposed ESIM-MBM and ESS-TTAs-MBM with different MBM-based IM schemes are analyzed, and the theoretical average bit error probability (BEP) is also formulated and shown to match well with the Monte-Carlo simulation results at different TAs and SEs in the high SNR region. Finally, a significant improvement in the SE and bit error performance of the proposed schemes is demonstrated with other classic MBM-based IM schemes such as quadrature spatial modulation assisted MBM (QSM-MBM) and quadrature channel modulation (QCM).

Reconfigurable intelligent surfaces (RIS) have received significant attention in the field of communication and wireless networks, due to their numerous advantages, such as low cost, low energy consumption, miniaturization, programmability, and ease of deployment. This paper proposes an RIS based on liquid crystal (LC) for constructing wireless communication systems. Tunable LC embedded between metal microstructures supported by quartz substrates allows for precise control of the reflected wave phase. The experimental results demonstrate that RIS has more than 3600 of phase modulation capability and four stable digital coding states at the optimal operating frequency of 100.2 GHz. In addition, the RIS has a 100∘ beam scanning capability, providing a solid foundation for tracking and targeting during communications. The communication capability of RIS was then demonstrated in the sub-terahertz band by constructing a 16QAM constellation diagram test system and a video real-time transmission system with varying deflection angles. Our work provides excellent solutions for communication and wireless network applications in the millimeter wave and terahertz (THz) frequency bands.

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.

A comparison between the use of PAM and QAM constellations in Rate-Splitting Multiple Access (RSMA) is presented. Previous studies of RSMA with discrete or finite input signaling have considered either QAM or PSK. We propose the use of PAM constellations for every stream, with a phase rotation of 90 degrees applied to the common stream to achieve orthogonality with private streams. Numerical results based on data rate and Symbol Error Rate (SER) analysis indicate that PAM is superior to QAM when there is a significant disparity between channels for a two-user scenario. This finding indicates that PAM constellations can extend the operational region of RSMA over other Multiple Access (MA) strategies.

As telecommunication systems evolve to meet increasing demands, integrating deep neural networks (DNNs) has shown promise in enhancing performance. However, the trade-off between accuracy and flexibility remains challenging when replacing traditional receivers with DNNs. This paper introduces a novel probabilistic framework that allows a single DNN demapper to demap multiple QAM and APSK constellations simultaneously. It is demonstrated that the framework can exploit hierarchical relationships in families of constellations. The consequence is that we need fewer neural network outputs to encode the same function without an increase in Bit Error Rate (BER). The simulation results confirm that the framework approaches the optimal demodulation error bound under an Additive White Gaussian Noise (AWGN) channel for multiple constellations. Under 3GPP-compliant OFDM fading channels, it is as accurate as a neural receiver operating on just one modulation type. Thereby, the framework addresses multiple important issues in practical neural receiver design. These include improvements in computational efficiency, a reduction in memory overhead, and an improved adaptability in dynamic environments.

In this paper, an efficient soft-output fixedcomplexity sphere decoder (FSD), which is suitable for multipleinput multiple-output (MIMO) detection using large quadrature amplitude modulation (QAM) constellations, has been investigated. The sector-shaped enumeration algorithm, which stores the enumeration orders for both the real and the imaginary axes of the signal space, is designed for the first two layers of the proposed FSD. Meanwhile, the section-wise list candidate selection, employing a novel grouping strategy, is tailored for the leaf layer of the proposed FSD. Together, these techniques effectively lower computational complexity and substantially reduce time delays. In addition, the log-likelihood ratio (LLR) refinement is explored to address the overestimation caused by the bit-flipping algorithm, significantly improving error performance. Finally, several hardware designs are explored to further minimize both time and area complexity in hardware implementation. To the best of our knowledge, this is the first 8 × 8 1024-QAM MIMO detector implemented using an FSD-based algorithm. It is demonstrated that the proposed soft-output FSD achieves error performance comparable to that of the list sphere decoder, while also offering significantly reduced complexity compared to prior research in the literature.

Today, the anywhere, anyhow and anytime application scenarios of 5G system force designer to challenge on electromagnetic interference (EMI) requirements.Despite the technological progress, relevant test techniques are necessary to minimize the future communication system EMI risk.In this paper, the EMI characterization from nonlinearity (NLT) of 5G system Gallium Nitride (GaN) power amplifier (PA) is studied.Firstly, the PA NLT is evaluated by 1-dB/3-dB/6-dB compression point and 3rd-order intermodulation distortion (IMD3).Then, a measurement platform is built based on vector signal generator and EMI receiver including digital modulation system.According to the adjacent channel leakage ratio (ACLR), error vector magnitude (EVM) and signal-tonoise ratio (SNR), the EMI characteristics of 3.5-GHz carrier signals modulated by 16-Quadrature Amplitude Modulation (16-QAM) distorted by the GaN PA NLT are discussed.Due to the GaN PA 3rd order intermodulation (IM3) product, the SNR degrades from 34.8 dB to 14.6 dB when the input signal power increases from -10 dBm to 6 dBm.The EMI effect is confirmed by significant signal distortion observed with 16-QAM constellation diagram.Research work is currently ongoing for extending the EMI test technique for 6G communication system.

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff.

In this paper, in view of merit of both the index modulation (IM) and the spatial channel modulation (CM), a new scheme, named as spatial channel modulation with multidimensional constellation (SCM-MDC), is designed to enhance the throughput and the reliability of communication systems. In the SCM-MDC, according to the employment of the signal constellations, it has two cases: Firstly, if modulating the QAM constellation, the antenna index (AI) vector set A and B are constructed by one active transmit antenna. Secondly, if modulating the 3D signal constellation, the AI vector set A and B are constructed by two and one active transmit antenna, respectively. Then, in order to be capable of enhancing the additional information, each transmit antenna is equipped with several radio frequency mirrors for carrying channel index information. Furthermore, the spectral efficiency and the bit error probability are provided. In the comparisons of the simulation results with the bit error rate (BER) performance, it can be seen that, the SCM-MDC achieves the better BER performance than the SM-MBM and QSM-MBM schemes.

Abstract In this paper, making full use of the spatial domain of transmit antennas (TAs) and keeping the characteristic of the squared minimum Euclidean distance (MED) between the transmitted spatial vectors (TSVs), generalized enhanced spatial modulation with signal points allocation (GESM‐SPA) is proposed to expand the size of signal spaces for enhancing the spectral efficiency and the reliability of communications. In the GESM‐SPA, according to the number of active TAs, signal constellation points (CPs) from the QAM or secondary QAM constellations are allocated and then modulated on the corresponding active TAs with the selected antenna index (AI) vector. Through this design, which further exploits the spatial domain with the variability of active TAs, the squared MED between the TSVs is increased in comparison with the existing traditional systems. More specifically, in view of the disadvantage of the classic ESM system, the constellation groups (CGs) with priority given to the QAM CPs are constructed to further maximize the squared MED. Then, the AI vector subsets corresponding to the obtained CGs are designed to be candidate for the specified AI vector set with the AI information. The squared MED and the average bit error probability (BEP) are analyzed. In simulation results using Monte Carlo, the GESM‐SPA outperforms the existing classic systems in terms of the bit error rate performance.

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO-OFDM systems. To exploit the structure information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training in time domain. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Furthermore, an online learning-based module is devised to compensate the nonlinear distortion caused by RF circuit components. On top of that, a learning-efficient classifier named StructNet is introduced in frequency domain to further improve the symbol detection performance by utilizing the QAM constellation structural pattern. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques under dynamic channel environment and RF circuit nonlinear distortion. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.

In this work, a noise-immune 16-QAM demodulator in a digital television system of the DVB-T2 standard is investigated and modeled using the Matlab/Simulink software environment. The requirements for normalized technical parameters that determine the quality and noise immunity of a 16-QAM demodulator in the DVB-T2 standard have been analyzed and investigated. Various communication channels are modeled and investigated, such as: the Gaussian, Rice and Rayleigh channel to determine and measure the signal-to-noise ratio, the theoretical and practical influence of communication channels on noise immunity without a filter and with a filter. The results of researches on the dependence of the bit error probability and the number of bits received with an error on Eb/N0, the spectrum of 16-QAM signals and constellation diagrams are presented.

As the demand for higher data throughput in coherent optical communication systems increases, we need to find ways to increase capacity in existing and future optical communication links. To address the demand for higher spectral efficiencies, we apply end-to-end optimization for joint geometric and probabilistic constellation shaping in the presence of Wiener phase noise and carrier phase estimation. Our approach follows state-of-the-art bitwise auto-encoders, which require a differentiable implementation of all operations between transmitter and receiver, including the DSP algorithms. In this work, we show how to modify the ubiquitous blind phase search (BPS) algorithm, a popular carrier phase estimation algorithm, to make it differentiable and include it in the end-to-end constellation shaping. By leveraging joint geometric and probabilistic constellation shaping, we are able to obtain a robust and pilot-free modulation scheme improving the performance of 64-ary communication systems by at least 0.1 bit/symbol compared to square QAM constellations with neural demappers and by 0.05 bit/symbol compared to previously presented approaches applying only geometric constellation shaping.

Abstract In this article, three mirror activation pattern (MAP) selection techniques are used to improve the performance of energy‐efficient hexagonal quadrature amplitude modulation (HQAM) aided media‐based modulation (MBM) system called HMBM. These three MAP selection (MS) aided HQAM techniques: (i) capacity‐optimized MS‐based HMBM (CMS‐HMBM) scheme, (ii) MAP correlation (MC) aided MS‐based HMBM (MMS‐HMBM) scheme, (iii) Euclidean distance (ED) based MS aided HMBM (EMS‐HMBM) scheme. MBM technique, one of the newest and most competitive candidates of the index modulation family, increases spectral efficiency linearly and significantly improves error performance. When MS techniques are integrated into telecommunication techniques, they significantly increase the performance of the system. Extensive computer simulations with Monte Carlo for the considered systems are performed on Rayleigh fading channels. The suggested system offers superior error performance than the conventional MBM scheme since it incorporates CMS, MMS, and EMS techniques. Additionally, the suggested new method offers greater energy efficiency than ‐QAM constellation schemes since HQAM constellation schemes are by their very nature high energy efficiency. It is shown that the proposed CMS‐HMBM, MMS‐HMBM, and EMS‐HMBM systems provide better error performance than capacity‐optimized antenna selection (COAS) aided hexagonal spatial modulation (COAS‐HSM), antenna correlation‐based AS aided HSM (ACAS‐HSM), Euclidean distance optimized AS aided HSM (EDAS‐HSM), SM, and MBM systems for the same spectral efficiency. The suggested systems are envisaged for usage in sixth‐generation and later wireless networks because of their high performance provided by the CMS, MMS, and EMS methods in the proposed system and the high spectral efficiency provided by the HMBM approach.