Fluid Antenna Grouping-Based Index Modulation: Transceiver Design for MIMO Systems

The fluid antenna (FA)-enabled multiple-input multiple-output (MIMO) system based on index modulation (IM), referred to as FA-IM, significantly enhances spectral efficiency (SE) compared to the conventional FA-assisted MIMO system. This paper proposes an innovative FA grouping-based IM (FAG-IM) system to improve performance in mitigating the high spatial correlation between multiple activated ports. A block grouping scheme is employed based on the spatial correlation model and the distribution structure of the ports. Then, a closed-form expression for the average bit error probability (ABEP) upper bound of the FAG-IM system is derived. In order to reduce the receiver complexity of the proposed system, the message passing mechanism is first incorporated into the FAG-IM system. Subsequently, within the approximate message passing (AMP) framework, an efficient structured AMP (S-AMP) detector is devised by leveraging the structural characteristics of the transmission signal vector. Simulation results confirm that the proposed FAG-IM system significantly outperforms the existing FA-IM system in the presence of spatial correlation. The derived ABEP curve aligns well with the numerical results, providing an efficient theoretical tool for evaluating the system performance. Additionally, simulation results demonstrate that the proposed low-complexity S-AMP detector not only reduces the time complexity to a linear scale but also substantially improves bit error rate (BER) performance compared to the minimum mean square error (MMSE) detector, thus facilitating the practical implementation of the FAG-IM system.

Decentralized Hybrid Precoding for Massive MU-MIMO ISAC

Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramer-Rao bound (CRB) as an objective function.Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on ThomlinsonHarashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.

Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., $1.73$ dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from $64.89\%$ to $70.31\%$ for classification.

Joint Antenna Position and Beamforming Optimization with Self-Interference Mitigation in MA-ISAC system

Beamforming design has been extensively investigated in integrated sensing and communication (ISAC) systems. The use of movable antennas has proven effective in enhancing the design of beamforming. Although some studies have explored joint optimization of transmit beamforming matrices and antenna positions in bistatic scenarios, there is a gap in the literature regarding monostatic full-duplex (FD) systems. To fill this gap, we propose an algorithm that jointly optimizes the beamforming and antenna positions at both the transmitter and the receiver in a monostatic FD system. In an FD system, suppressing self-interference is crucial. This interference can be significantly reduced by carefully designing transmit and receive beamforming matrices. To further enhance the suppression, we derive a formulation of self-interference characterized by antenna position vectors. This enables the strategic positioning of movable antennas to further mitigate interference. Our approach optimizes the weighted sum of communication capacity and mutual information by simultaneously optimizing beamforming and antenna positions for both tranceivers. Specifically, we propose a coarse-to-fine grained search algorithm (CFGS) to find optimal antenna positions. Numerical results demonstrate that our proposed algorithm provides significant improvements for the MA system compared to conventional fixed-position antenna systems.

Quantum Long Short-Term Memory for Drug Discovery

Quantum computing combined with machine learning (ML) is an extremely promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we successfully apply QML to drug discovery, showing that QML can significantly improve model performance and achieve faster convergence compared to classical ML. Moreover, we demonstrate that the model accuracy of the QML improves as the number of qubits increases. We also introduce noise to the QML model and find that it has little effect on our experimental conclusions, illustrating the high robustness of the QML model. This work highlights the potential application of quantum computing to yield significant benefits for scientific advancement as the qubit quantity increase and quality improvement in the future.

Fluid Antenna Grouping Index Modulation Design for MIMO Systems

Index modulation (IM) significantly enhances the spectral efficiency of fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) systems, which is named FA-IM. However, due to the dense distribution of ports on fluid antennas, the wireless channel exhibits a high spatial correlation, resulting in severe performance degradation in the existing FA-IM scheme. This paper proposes a novel fluid antenna grouping index modulation (FA-GIM) scheme to mitigate the spatial correlation of the FA-IM channel, further enhancing system performance. Based on the spatial correlation model of two-dimensional (2D) fluid antenna surfaces, this paper specifically adopts a block grouping method where adjacent ports are allocated to the same group. The numerical results demonstrate that the proposed scheme exhibits superior bit error rate (BER) performance compared to the state-of-the-art scheme, enhancing the robustness of FA-assisted MIMO systems.

PAPR Reduction with Pre-chirp Selection for Affine Frequency Division Multiple

Affine frequency division multiplexing (AFDM) is a promising new multicarrier technique based on discrete affine Fourier transform (DAFT). By properly tuning pre-chirp parameter and post-chirp parameter in the DAFT, the effective channel in the DAFT domain can completely avoid overlap of different paths, thus constitutes a full representation of delay-Doppler profile, which significantly improves the system performance in high mobility scenarios. However, AFDM has the crucial problem of high peak-to-average power ratio (PAPR) caused by phase randomness of modulated symbols. In this letter, an algorithm named grouped pre-chirp selection (GPS) is proposed to reduce the PAPR by changing the value of pre-chirp parameter on sub-carriers group by group. Specifically, it is demonstrated first that the important properties of AFDM system are maintained when implementing GPS. Secondly, we elaborate the operation steps of GPS algorithm, illustrating its effect on PAPR reduction and its advantage in terms of computational complexity compared with the ungrouped approach. Finally, simulation results of PAPR reduction in the form of complementary cumulative distribution function (CCDF) show the effectiveness of the proposed GPS algorithm.

Detecting Abrupt Change of Channel Covariance Matrix in IRS-Assisted Communication

The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting of a single-antenna user (UE), an IRS, and a multi-antenna base station (BS). We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix. Secondly, a powerful method is proposed to detect whether a Type I change occurs, a Type II change occurs, or no change occurs. The effectiveness of our proposed scheme is verified by numerical results.

Reconfigurable Intelligent Surface-Based Receive Generalized Spatial Modulation Design

In this paper, the receive generalized spatial modulation (RGSM) scheme with reconfigurable intelligent surfaces (RIS) assistance is proposed. The RIS group controllers change the reflected phases of the RIS elements to achieve the selection of receive antennas and phase shift keying (PSK) modulation, and the amplitudes of the received symbols are adjusted by changing the activation states of the elements to achieve amplitude phase shift keying (APSK) modulation. Compared with the existing RIS-aided receive generalized space shift keying (RIS-RGSSK) scheme, the proposed scheme realizes that the selected antennas respectively receive different modulation symbols, and only adds the process to control the modulated phases and the activation states of elements. The proposed scheme has better bit error rate (BER) performance than the RIS-RGSSK scheme at the same rate. In addition, the results show that for low modulation orders, the proposed scheme will perform better with PSK, while for high modulation order, APSK is better. The proposed scheme is a promising scheme for future wireless communication to achieve high-efficiency.

Capacity-based Spatial Modulation Constellation and Pre-scaling Design

Spatial Modulation (SM) can utilize the index of the transmit antenna (TA) to transmit additional information. In this paper, to improve the performance of SM, a non-uniform constellation (NUC) and pre-scaling coefficients optimization design scheme is proposed. The bit-interleaved coded modulation (BICM) capacity calculation formula of SM system is firstly derived. The constellation and pre-scaling coefficients are optimized by maximizing the BICM capacity without channel state information (CSI) feedback. Optimization results are given for the multiple-input-single-output (MISO) system with Rayleigh channel. Simulation result shows the proposed scheme provides a meaningful performance gain compared to conventional SM system without CSI feedback. The proposed optimization design scheme can be a promising technology for future 6G to achieve high-efficiency.