Reconfigurable Intelligent Surfaces (RIS) are a promising technology for creating smart radio environments by controlling wireless propagation. However, several factors hinder the integration of RIS technology into existing cellular networks, including the incompatibility of RIS control interfaces with 5G PHY/MAC procedures for synchronizing radio scheduling decisions and RIS operation, and the cost and energy limitations of passive RIS technology. This paper presents RISENSE, a system for practical RIS integration in cellular networks. First, we propose a novel, low-cost, and low-power RIS design capable of decoding control messages without complex baseband operations or additional RF chains, utilizing a power sensor and a network of microstrip lines and couplers. Second, we design an effective in-band wireless RIS control interface, compatible with 5G PHY/MAC procedures, that embeds amplitude-modulated (AM) RIS control commands directly into standard OFDM-modulated 5G data channels. Finally, we propose a low-overhead protocol that supports swift on-demand RIS re-configurability, making it adaptable to varying channel conditions and user mobility, while minimizing the wastage of 5G OFDM symbols. Our experiments validate the design of RISENSE and our evaluation shows that our system can re-configure a RIS at the same pace as users move, boosting 5G coverage where static or slow RIS controllers cannot.

Reconfigurable Intelligent Surfaces (RISs) have shown great prospects in securing mmWave communication from potential eavesdropping by configuring reflecting elements to strengthen the signal strength at the desired location and creating nulls at potential eavesdropping locations. Acquiring perfect channel information is crucial for optimizing RIS configuration; however, obtaining such information is costly and, as a result, should be performed sparingly. This work studies the impact of the age of channel information on the secrecy performance of a RIS-assisted mmWave network. In particular, we investigate how outdated channel information affects the joint optimization of transmit beamforming and RIS configuration. In our Monte-Carlo simulations, we first identify the factors influencing the aging process of a RIS-assisted mmWave channel in both the near and far fields of the RIS. Subsequently, we examine the impact of channel aging on secrecy capacity and demonstrate that adequate secrecy capacity can still be achieved even when channel information is slightly outdated, thus reducing the need for frequent RIS configuration.

Orthogonal Time Frequency Space (OTFS) modulation is widely recognized as a modulation scheme with advan-tageous properties for both radar and communication waveform designs. In OTFS, information symbols are mapped in the Delay-Doppler (DD) domain leading to reliable communication in high Doppler channels. However, the presence of inverse-discrete Fourier Transform operation in OTFS architecture results in a high Peak-to-Average Power Ratio (PAPR) in the transmit OTFS frames. This paper introduces a novel metric-based symbol pre-distortion algorithm constrained by Error Vector Magnitude (EVM) limits to reduce the PAPR in OTFS modulation. The imposed constraint in the form of EVM limits establishes it as a pragmatic method that adds no side-channel information rather exploits the available EVM limit usually kept in wireless standards. The pre-distortion applied to each symbol/sample in an OTFS frame is determined by the proposed metrics, representing the contribution of each symbol to peak values in the output. The proposed method is simple, flexible, and does not require additional complexity for symbol detection on the receiver end. Our simulation results demonstrate a significant reduction in the PAPR of OTFS blocks for both QPSK and 16-QAM modulation schemes. Furthermore, our proposed Constrained Constellation Shaping scheme exhibits enhanced performance in PAPR reduction as the number of Doppler bins increases for an OTFS frame size.

Reconfigurable intelligent surfaces (RISs) have great potential to improve the coverage of mmWave networks; however, acquiring perfect channel state information (CSI) of a RIS-enabled mmWave network is very costly and should thus be done infrequently. At the same time, finding an optimal RIS configuration when CSI is outdated is challenging. To this end, this work aims to provide practical insights into the tradeoff between the outdatedness of the CSI and the system performance by using the effective capacity as analytical tool. We consider a RIS-enabled mmWave downlink where the base station (BS) operates under statistical quality-of-service (QoS) constraints. We find a closed-form expression for the effective capacity that incorporates the degree of optimism of packet scheduling and correlation strength between instantaneous and outdated CSI. Moreover, our analysis allows us to find optimal values of the signal-to-interference-plus-noise-ratio (SINR) distribution parameter and their impact on the effective capacity in different network scenarios. Simulation results demonstrate that better effective capacity can be achieved with suboptimal RIS configuration when the channel estimates are known to be outdated. It allows us to design system parameters that guarantee better performance while keeping the complexity and cost associated with channel estimation to a minimum.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resource-constraint nature of IoT devices.