Efficient Blind Detection Clustering Algorithm for Reconfigurable Intelligent Surface-aided Spatial Modulation Systems
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摘要: 针对智能反射面(RIS)辅助空间调制(SM)系统(RIS-SM)在未知信道状态信息(CSI)条件下的信号检测问题，该文提出一种新型无监督聚类检测算法。考虑到RIS的无源特性及传统检测方法对完美CSI的依赖难以满足实际部署需求，将RIS-SM系统的盲检测任务转化为聚类问题，并在K-means算法基础上引入信道统计特性指导的初始化机制。该方法有效利用RIS-SM系统中等效信道的幅度与相位分布特征，在不依赖任何先验CSI的条件下，实现高效且低复杂度的信号检测。仿真结果验证了所提算法在多种系统配置下均可逼近最优最大似然(ML)性能，充分展示了其在理论研究与实际应用中的可行性与优势。Abstract:
Objective Reconfigurable Intelligent Surface (RIS)-aided Spatial Modulation (SM) systems (RIS-SM) represent an advanced integration of two promising technologies, offering considerable potential to enhance spectral and energy efficiency in future Sixth-Generation (6G) networks. However, the passive nature of RIS poses a significant challenge to acquiring accurate Channel State Information (CSI), which is essential for conventional signal detection methods. Most traditional algorithms assume perfect CSI, an unrealistic assumption in practical deployments that constrains both performance and scalability. To overcome this limitation, this study proposes an enhanced K-means clustering detection algorithm that reframes the blind detection problem as a clustering task. By exploiting the statistical channel distribution properties inherent to RIS-SM systems, the proposed algorithm achieves efficient blind signal detection without prior CSI and with low computational complexity. These findings contribute to the optimization of RIS-SM system performance and support the development of robust blind detection strategies in complex wireless communication scenarios. Methods This study considers a typical RIS-SM architecture in which the RIS comprises multiple passive reflecting elements and the receiver is equipped with multiple antennas. The wireless channel is modeled as an equivalent channel derived from the unique propagation characteristics of the RIS-SM system. By exploiting the statistical distribution of the diagonal elements of this equivalent channel, an improved K-means clustering detection algorithm is proposed, eliminating the requirement for prior CSI. The algorithm’s performance is assessed through extensive simulations. The communication channel is modeled as a Rayleigh fading environment with Additive White Gaussian Noise (AWGN). Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) schemes are adopted. Bit Error Rate (BER) is used as the primary performance metric to compare the proposed approach against conventional detection methods. Simulations are carried out on the MATLAB platform. Results demonstrate that the proposed algorithm significantly lowers computational complexity while achieving near-optimal detection accuracy, highlighting its suitability for practical deployment in RIS-SM systems. Results and Discussions Simulation results indicate that the proposed improved K-means clustering detection algorithm markedly enhances the performance of RIS-SM systems under unknown CSI conditions. As illustrated in Fig. 4 andFig. 5 , the proposed method achieves BER performance that closely approaches that of ideal KMC (KMC(ideal)) and Maximum Likelihood (ML) detectors. It outperforms several benchmark algorithms, including traditional KMC, multi-initialization KMC (KMC($ P $)), improved KMC (IKMC), K-means++ (KMC++), and Constrained Clustering-Based Detection (CCBD). While repeated initialization in KMC ($ P $) can alleviate poor local minima, it substantially increases computational complexity. IKMC improves centroid initialization but yields limited enhancement in detection accuracy. KMC++ exhibits performance degradation at high Signal-to-Noise Ratio (SNR) due to persistent error floors. CCBD utilizes structural priors to improve clustering but shows performance deterioration when the number of received signals is small. Furthermore,Fig. 6 demonstrates that the proposed algorithm converges more rapidly than other clustering-based methods, particularly under low SNR conditions, highlighting its robustness and efficiency in noisy environments.Table 1 provides additional evidence that the proposed method achieves a favorable balance between detection accuracy and computational cost, supporting its practical value for blind detection in RIS-SM systems.Conclusions This study proposes an improved K-means clustering detection algorithm for RIS-SM systems, reformulating the blind detection task as an unsupervised clustering problem. By exploiting the distribution properties of the diagonal elements of the equivalent channel, the algorithm enhances the initialization of clustering centroids, enabling efficient blind signal detection without prior CSI. Simulation results confirm that the proposed method achieves near-optimal detection performance across a wide range of SNR levels and system configurations. These findings offer meaningful contributions to both the theoretical development and practical deployment of RIS-SM systems. The proposed approach is particularly relevant for the design of low-complexity and high-efficiency detection schemes in next-generation wireless networks, including Beyond-5G (B5G) and 6G systems. This work also provides a foundation for future research on blind detection algorithms and their integration with emerging technologies such as RIS, contributing to the advancement of intelligent and adaptive wireless communication systems. -
表 1 不同检测算法的复杂度比较
检测算法 计算复杂度 计算值 最大似然检测算法(ML) $ O(N_{\rm r}^2(M + N)L) $ $ 4.22 \times {10^5} $ 贪婪检测算法(GD) $ O(({N_{\rm r}} + M)L) $ $ 1.60 \times {10^3} $ 传统KMC检测算法 $ O({N_{\rm r}}M{T_1}L) $ $ 9.60 \times {10^3} $ KMC($ P $) $ O(P{N_{\rm r}}M{T_1}L) $ $ 9.60 \times {10^5} $ 改进的KMC检测算法(IKMC) $ O({L^2} + L{N_{\rm r}}M{T_2}) $ $ 4.32 \times {10^4} $ 约束聚类盲检测算法(CCBD) $ O(L{N}_{\rm r}M{T}_{3}) $ $ 6.40 \times {10^3} $ Kmeans++算法(KMC++) $ O(L{N}_{\rm r}M{T}_{4}) $ $ 3.20 \times {10^3} $ 所提出的检测算法(Proposed) $ O(L{N_{\rm r}}M{T_5}) $ $ 3.20 \times {10^3} $ 
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