The application of intelligent reflecting surfaces (IRSs) to suppress interference in wireless communication systems has recently attracted significant research attention. Most existing approaches rely on complete or partial channel state information (CSI) to configure the IRS. However, acquiring accurate CSI in IRS-assisted systems involves considerable pilot overhead and introduces non-negligible delays. This issue is further exacerbated under strong interference conditions, where interfering sources are typically non-cooperative, making CSI acquisition even more challenging. As a result, existing CSI-dependent interference suppression methods become difficult to deploy in practice. To address these limitations, we propose a novel blind interference suppression strategy that combines a proportional phase-inversion (PPI) algorithm with the conditional sample mean (CSM) method. The proposed approach determines the IRS configuration using only the received signal power, without requiring any prior CSI. We conduct a comprehensive performance evaluation by deriving the theoretical performance of the proposed scheme, which is subsequently verified through numerical simulations. Furthermore, simulation results across various parameter settings demonstrate that the proposed blind interference suppression scheme reduces the interference power to the level of noise, thereby achieving a marked signal-to-interference-plus-noise ratio (SINR) improvement and outperforms existing benchmark schemes.

In this paper, we investigate codebook-based near-field beam training for extremely large-scale intelligent reflecting surface (XL-IRS). Compared with the conventional far-field beam training method that only searches for the best beam direction, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead of two-dimensional exhaustive search, we propose a novel two-layer codebook-based near-field beam training scheme that decomposes the two-dimensional search into two sequential phases. Specifically, the layer-l codebook designed based on the omnidirectivity of random-phase beam pattern is firstly employed to estimate the user distance. Then, given the estimated user distance of the layer-1, a customized layer-2 codebook is employed to scan the candidate locations of the user. Numerical results demonstrate that the proposed scheme can achieve more accurate estimation of the user distance and angle, as well as higher data rate with smaller training overhead, compared with benchmarks.

Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) are expected to be key technologies in future wireless communication systems. With the ability of modifying the wireless channel, RIS has a great potential to improve the channel capacity and spectrum efficiency. However, the modification of wireless channel may change the optimal user pairing strategy of NOMA, which will cause large interference and decoding failure at the receiver. The joint optimization of user pairing and RIS beamforming in NOMA systems has not been studied yet. To address this issue, this paper considers a RIS assisted NOMA downlink transmission with one base station (BS), where the edge users are paired with the central users to share the same resource blocks, and RISs are deployed to serve the edge users. Our goal is to maximize the sum rate of edge users by jointly optimizing the user pairing, the passive beamforming at the RISs, the active beamforming and power allocation at the BS, subject to the users’ minimum rate and the total transmitting power. Since the formulated problem is non-convex and difficult to be solved, we propose an alternating iterative algorithm to obtain the suboptimal solution. Simulation results show that the proposed scheme can increase the sum rate of edge users by 5% and 7% compared to the other two baseline methods.

In this article, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users' access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user's location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.