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.

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.

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.

CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.

CDDM: Channel Denoising Diffusion Models for Wireless Communications

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). Experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.