In this paper, we present a novel communication system model that integrates reconfigurable intelligent surfaces (RIS), spatial shift keying (SSK), and code index modulation (CIM) based on Hadamard coding called RIS based transmit SSK-CIM (RIS-CIM-TSSK). By leveraging RIS, the system adapts rapidly to dynamic environments, enhancing error rates and overall reliability. SSK facilitates the transmission of additional passive information while eliminating the need for multiple radio frequency (RF) chains, thereby reducing complexity. CIM enhances passive information transmission through frequency domain spreading, which may increase signal obfuscation. This proposed scheme not only improves energy efficiency but also offers a robust solution for reliable communication in modern wireless networks, paving the way for smarter and more adaptable implementations. We consider a suboptimal, low-complexity detector for the proposed scheme and also address the blind case for phase adjustment of the RIS. Finally, we present the simulation results for the proposed system model across various configurations, including different numbers of receive and transmit antennas, varying reflecting elements of the RIS, and different code lengths.

Network slicing is a key enabler for providing differentiated service support to heterogeneous use cases and applications in 5G and beyond networks through creating multiple logical slices. Resource management for satisfying diverse requirements of slices is a highly challenging task under time-varying traffic and wireless channel conditions. This paper presents a deep reinforcement learning (DRL) based xApp for dynamically managing the service level agreements (SLAs) of network slices, eliminating the need for human-in-the-loop decision-making. The proposed xApp is implemented within an open source mobile network emulator to create an O-RAN compliant end-to-end 5G network capable of dynamic resource management capabilities. The intelligent resource management xApp operates on the RAN Intelligent Controller (RIC), enabling monitoring and dynamic resource control of the gNodeB through the E2 interface. The proof-of-concept experiment results demonstrate that the trained DRL model deployed on the RIC platform is successfully utilized to manage the key performance indicators of the network slices.

Reconfigurable intelligent surface (RIS) structures reflect incident signals by adjusting phase adaptively according to the channel condition during transmission to increase signal quality at the receiver. Spatial modulation (SM) technique is a potential candidate for future energy-efficient wireless communications due to its ability to provide better throughput, low-cost implementation, and good error performance. Also, Alamouti's space-time block coding (ASBC) is an important space and time coding technique for the diversity gain and simplified maximum likelihood (ML) detection. In this paper, we propose the RIS-assisted received spatial modulation (RSM) scheme with ASBC, namely RIS-RSM-ASBC, for next-generation wireless networks. The RIS in the proposed system model is separated into two parts, each used as an access point (AP) to transmit its Alamouti coded information while reflecting passive signals to the selected receive antenna. We design the optimal ML detector for the proposed RIS-RSM-ASBC scheme. Furthermore, we present the greedy detector (GD) to reduce the complexity of the ML detector. Extensive computer simulations are performed to corroborate theoretical derivations. The results show that the RIS-RSM-ASBC system is highly reliable and provides data rate enhancement compared to traditional RIS-assisted transmit SM (RIS-TSM), RIS-assisted transmit quadrature SM (RIS-TQSM), RIS-assisted received SM (RIS-RSM), RIS-assisted transmit space shift keying with ASBC (RIS-TSSK-ASBC), and RIS-assisted TSSK with Vertical-Bell Laboratories Layered Space-Time (RIS-TSSK-VBLAST) schemes.