Semantic communications target to reliably convey the semantic meaning of messages. It is different from existing communication systems focusing on reliable bit transmission. To achieve the goal of semantic communications, we propose a signal shaping method by minimizing the semantic loss, which is measured by the pretrained bidirectional encoder representation from transformers (BERT) model. The signal set optimization problem for semantic communication systems with a few message candidates is investigated. We propose an efficient projected gradient descent method to solve the problem and prove its convergence. Simulation results show that the proposed method outperforms existing signal shaping methods in minimizing the semantic loss.

As a classical clustering model, Gaussian Mixture Model (GMM) can be the footstone of dominant machine learning methods like transfer learning. Evolving GMM is an approximation to the classical GMM under time-critical or memory-critical application scenarios. Such applications often have constraints on time-to-answer or high data volume, and raise high computation demand. A prominent approach to address the demand is GPGPU-powered computing. However, the existing evolving GMM algorithms are confronted with a dilemma between clustering accuracy and parallelism. Point-wise algorithms achieve high accuracy but exhibit limited parallelism due to point-evolutionary pattern. Block-wise algorithms tend to exhibit higher parallelism. Whereas, it is challenging to achieve high accuracy under a block-evolutionary pattern due to the fact that it is difficult to track evolving process of the mixture model in fine granularity. Consequently, the existing block-wise algorithm suffers from significant accuracy degradation, compared to its batch-mode counterpart: the standard EM algorithm. To cope with this dilemma, we focus on the accuracy issue and develop an improved block-evolutionary GMM algorithm for GPGPU-powered computing systems. Our algorithm leverages evolving history of the model to estimate the latest model order in each incremental clustering step. With this model order as a constraint, we can perform similarity test in an elastic manner. Finally, we analyze the evolving history of both mixture components and the data points, and propose our method to merge similar components. Experiments on real images show that our algorithm significantly improves accuracy of the original general purpose bock-wise algorithm. The accuracy of our algorithm is at least comparable to that of the standard EM algorithm and even outperforms the latter under certain scenarios.

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER.

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mm ave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (F-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, we not only use the selected beamspace to transmit information but also use the selection operation to carry information. e prove that GBMM is superior to BBS in terms of SE and can break through the well known upper bound. That is, GBMM renews the upper bound of the system SE. e investigate SE-oriented precoder activation probability optimization, fully-digital precoder design and hybrid precoder design for GBMM. Comparisons with the benchmark (i.e., F-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.

This paper proposes a generalized beamspace modulation using multiplexing (GBMM) scheme for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications with reduced radio frequency (RF) chains. Besides achieving a multiplexing gain over the selected beamspace set, GBMM additionally makes use of the index of the beamspace set to carry information. In the proposed GBMM, the beamspace sets are non-uniformly activated. We investigate the spectral efficiency (SE) of the proposed GBMM and the SE-oriented beamspace set activation probability optimization as well as the hybrid precoder design. In the hybrid precoder design procedure, we first design the fully-digital precoders and then adopt the optimized fully-digital precoders to design the hybrid precoders. A gradient ascent algorithm is developed to find the optimal fully-digital precoders and precoder activation probabilities. In the high signal-to-noise-ratio (SNR) regime, closed-form solutions of the fully-digital precoders and the precoder activation probabilities are derived. Moreover, we investigate the impact of the hybrid receiver structure on the performance of GBMM, propose a coding method to realize the optimized precoder activation, and discuss the extension to orthogonal frequency division multiplexing (OFDM)-based mmWave broadband communications. Both analytical and numerical results show that GBMM outperforms the spatial multiplexing over the best beamspace set in terms of SE, which has been well recognized as the best transmission solution in mmWave MIMO communications.

This paper investigates the generic signal shaping methods for the multiple-data-stream generalized spatial modulation (GenSM) and the generalized quadrature spatial modulation (GenQSM). Three cases with different channel state information at the transmitter (CSIT) are considered, including no CSIT, statistical CSIT, and perfect CSIT. A unified optimization problem is formulated to find the optimal transmit vector set under size, power, and sparsity constraints. We propose an optimization-based signal shaping (OBSS) approach by solving the formulated problem directly and a codebook-based signal shaping (CBSS) approach by finding the sub-optimal solutions in discrete space. In the OBSS approach, we reformulate the original problem to optimize the signal constellations used for each transmit antenna combination (TAC). Both the size and the entry of all signal constellations are optimized. Specifically, we suggest the use of a recursive design for the size optimization. The entry optimization is formulated as a non-convex large-scale quadratically constrained quadratic programming (QCQP) problem and can be solved by the existing optimization techniques with rather high complexity. To reduce the complexity, we propose the CBSS approach using a codebook generated by the quadrature amplitude modulation (QAM) symbols and a low-complexity selection algorithm to choose the optimal transmit vector set. The simulation results show that the OBSS approach exhibits the optimal performance in comparison with existing benchmarks. However, the OBSS approach is impractical for large-size signal shaping and adaptive signal shaping with instantaneous CSIT due to the demand of high computational complexity. As a low-complexity approach, the CBSS shows comparable performance and can be easily implemented in large-size systems.

Spatial modulation (SM) conveys information bits by utilizing both the antenna index and complex symbols to form a 3-D constellation. Similar to 2-D modulation, the structure of 3-D constellation could greatly affect the transmission reliability. In this paper, a generalized 3-D constellation design is proposed to optimize the constellation diagram used for each antenna, i.e., to optimize the complex symbols and their total number for each antenna and finally to enhance the transmission reliability. The optimal design method with exhaustive search algorithm may cause prohibitive computational complexity, especially when the cardinality of 3-D constellation is large. To overcome this issue, a recursive design algorithm is proposed with a computational complexity increasing polynomially with the cardinality of 3-D constellation. Extensions of the proposed methods to the SM constellation design for massive multiple-input multiple-output transmission, generalized spatial modulation (GSM) constellation design, and the SM constellation design with transmit antenna correlation are also discussed. Simulations are done to validate those theoretical analysis, and results show that the proposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSM systems without constraints on the number of transceiver antennas. It is also shown that the proposed approach offers better symbol-error-rate performance than other solutions.

Multilevel coded I-Q modulation (I-Q MCM) is proposed in this paper. By utilizing the orthogonal character of QAM modulation, the in-phase and quadrature components of the signal can be encoded and decoded independently in I-Q MCM, which can greatly decrease the decoding complexity and time delay. Through simulation, we can also see this I-Q MCM doesn't give rise to any performance loss compared with traditional MCM scheme.

Based on different partitioning rules for MQAM constellations. Nonstandard MLC/MQAM schemes had been proposed to reduce the calculation of the individual capacity. In this paper, performance of the proposed MLC/MQAM schemes in well-defined OFDM systems are discussed with punctured convolutional codes as component codes. Compared with traditional MLC/MQAM and MQAM without MLC in OFDM systems, simulation results show that the nonstandard MLC/MQAM schemes can get better performance and lower complexity.

Optimal multilevel codes (MLC) with QAM constellations are considered with focus on both AWGN and Rayleigh fading channels. With the proposition of three novel set partitioning rules for QAM constellations, a new MLC scheme with a very simple MSD (multistage decoding) structure comes into being, which is operated on the capacity rule. Compared with traditional MLC scheme the new MLC structure greatly reduces the calculation of the individual capacities in the MLC system because the new set partitioning rules ensure the independency of two dimension symbols. Furthermore the new system cannot cause any performance loss and the time delay is just 1/2 of that with traditional partitioning rules. As an example 64QAM constellation with three new set partitioning rules are provided. Their performance is simulated over AWGN and Rayleigh fading channels by choosing BCH codes as the component codes.

In this paper some nonstandard partitioning rules for 16QAM constellation are proposed. With these nonstandard partitioning rules a new MSD structure is generated. For the fixed component codes length the new structure can greatly reduce the complexity of MLC(Multilevel codes) system. The time delay is just 1/2 of that with traditional partitioning rules. Simulation results show that the new partitioning rules and the new MSD structure do not create any performance loss. Furthermore for the fixed time delay the new MLC system is better than the traditional MLC system and the performance improvement is about 0.25 dB with BER equal to 10/sup -5/.

In this paper, based on the characteristics of multilevel codes (MLC) and space-time block codes (STBC), the concatenation scheme combining MLC and STBC is proposed over Rayleigh fading channels. According to the fact that MLC system can achieve optimal performance according to the "capacity rule and BP (block partitioning) rule", the same topics, i.e. the optimal scheme involving in the optimal code rate design and the optimal set partitioning of MLC-STBC system are investigated in this paper. After analysis and computer simulations, a novel criterion for optimum MLC-STBC is proposed.

In this paper, based on the characteristics of multilevel codes (MLC) and space-time block codes (STBC), a concatenation scheme (MLC-STBC) combining MLC and STBC is presented over Rayleigh fading channels. The unequal error protection (UEP) of MLC-STBC in Rayleigh fading channels for image transmission is investigated. Furthermore, taking 8ASK modulation as the example, a non-uniform signal constellation for realizing UEP of MLC-STBC is designed. Both theoretical analysis and computer simulation show that MLC-STBC scheme is a high rate and high power efficiency scheme, UEP is its inherent character and non-uniform signal constellation can improve its UEP ability. Its UEP character can enhance the transmission quality in the same bandwidth over a Rayleigh fading channel. MLC-STBC is an excellent scheme for image transmission.

A new multilevel code (MLC) structure with multiple antennas is presented, in which space diversity is achieved by using space-time block codes. Through performance analysis and computer simulation the asymptotic behavior of the new MLC scheme is provided, which is proportional to the product of the space diversity S and the minimum Hamming distance L' of component codes. When BCH codes are chosen as component codes and block partitioning (BP) with 8ASK modulation is adopted, the performances of the new MLC approach and the original MLC scheme are compared. The simulation results show that with the same bandwidth efficiency the new MLC scheme has a better power efficiency than the original MLC system without space diversity and the new system can achieve better trade-off between performance and complexity than the original MLC system without space diversity.