This paper investigates a stacked intelligent metasurfaces (SIM)-assisted integrated sensing and communications (ISAC) system. An extended target model is considered, where the base station (BS) aims to estimate the complete target response matrix relative to the SIM. Under the constraints of minimum signal-to-interference-plus-noise ratio (SINR) for the communication users (CUs) and maximum transmit power, we jointly optimize the transmit beamforming at the BS and the end-to-end transmission matrix of the SIM, to minimize the Cramér-Rao bound (CRB) for target estimation. Effective algorithms such as alternating optimization (AO) and semidefinite relaxation (SDR) are employed to solve the non-convex SINR-constrained CRB minimization problem. Finally, we design and build a hardware platform for SIM, and experimentally evaluate the performance of SIM-aided communication and sensing tasks.

Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the performance of physical-layer key generation (PKG) thanks to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the base station (BS) and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment and neglecting the existence of channel spatial correlation would possibly degrade the PKG performance. In this paper, we establish a general spatially correlated channel model and propose a new channel probing framework based on the transmit and the reflective beamforming. We derive a closed-form key generation rate (KGR) expression and formulate an optimization problem, which is solved by using the low-complexity Block Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation results show that compared to the existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB transmit power gain when the spacing between two neighboring RIS elements is a quarter of the wavelength. Also, the KGR increases significantly with the number of RIS elements while that increases marginally with the number of BS and user antennas.

Reconfigurable intelligent surface (RIS) is a promising technique to enhance the performance of physical-layer key generation (PKG) due to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the transmitter and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment. Also, neglecting the existence of channel spatial correlation would degrade the PKG performance. In this paper, we establish a general spatially correlated channel model in multi-antenna systems and propose a new PKG framework based on the transmit and the reflective beamforming at the base station (BS) and the RIS. Specifically, we derive a closed-form expression for characterizing the key generation rate (KGR) and obtain a globally optimal solution of the beamformers to maximize the KGR. Furthermore, we analyze the KGR performance difference between the one adopting the assumption of the i.i.d. model and that of the spatially correlated model. It is found that the beamforming designed for the correlated model outperforms that for the i.i.d. model while the KGR gain increases with the channel correlation. Simulation results show that compared to existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB performance gain when the BS antenna correlation$\rho$is 0.3 and the RIS element spacing is half of the wavelength.

Reconfigurable intelligent surfaces (RISs) are regarded as an emerging technology for the next generation of wireless communications. In this paper, we consider a multiple-input multiple-output network where each base station serves a user equipment with the aid of an RIS equipped with N reconfigurable elements. We characterize the interference at one user equipment that is caused by the signal emitted by its non-serving (interfering) RIS. By assuming Rayleigh fading channels, we study the corresponding interference-to-noise- ratio (INR) under the assumption of large values of N, and we prove that the INR is the product of a Chi-Square random variable (RV) and an RV that is approximated with a Gamma distribution. In addition, we prove that the amount of fading of the INR is equal to one in the large N regime.

In this paper, we consider non-coherent detection schemes for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

In this study, the authors first propose a sequence with the impulse‐like autocorrelation property named zero autocorrelation code (ZAC) for orthogonal frequency division multiplexing (OFDM) based on offset quadrature amplitude modulation (OQAM) systems. The ZAC sequence can be generated by many prototype filters such as physical layer for dynamic spectrum access and cognitive radio and isotropic orthogonal transform algorithm filters. Thus, it has a wide application range. The authors then propose a synchronisation algorithm based on the ZAC. The proposed algorithm only needs two symbols to compose the preamble while existing algorithms need at least four symbols. They further assess the synchronisation performances via computer simulation. The simulation results show that the proposed algorithm can achieve better performance in terms of root mean square error of the timing and frequency offset estimation, with a 10 dB improvement on timing offset estimation performance over Saeedi‐Sourck's algorithm when signal‐to‐noise ratio is 0 dB.

The filter bank multicarrier with offset quadrature amplitude modulation (FBMC‐OQAM) has aroused widespread concern in the 5G communication. However, FBMC‐OQAM has an inherent drawback of high peak‐to‐average power ratio (PAPR) that should be mitigated. Here, the authors propose an efficient hybrid scheme based on sparse partial transmit sequence (sparse PTS) scheme and tone reservation (TR) scheme to reduce the PAPR in FBMC‐OQAM systems. The first step is to detect and record the location of peak of FBMC‐OQAM signals and adopt phase rotation factors (based on PTS) to directly optimise the phase of recorded peak signals, which is called sparse PTS. Then the result is further processed by TR scheme for PAPR reduction. Simulation results show that the proposed hybrid scheme provides better PAPR reduction performance than the two schemes, and can achieve less side information transmitted than the sparse PTS scheme.

Visible light communication (VLC) system based on orthogonal frequency division multiplexing (OFDM) is promising for its ability to link a huge number of devices with high reliability and low latency. However, OFDM‐based VLC systems are very sensitive to the symbol timing offsets. Thus, synchronisation algorithm is very important for OFDM‐based VLC system. In this study, the authors first propose zero correlation code pair (ZCC pair) and investigate the periodical impulse‐shaped correlation property of ZCC pair. Then the authors propose a novel timing offset estimation algorithm based on ZCC pair. The preamble is processed via peak‐to‐average power ratio (PAPR) reduction algorithms to overcome the PAPR problem in VLC systems. This algorithm achieves high accuracy and has low synchronisation error probability. The effectiveness of the authors’ proposed algorithm has been demonstrated using computer simulation under non‐light‐of‐sight VLC and Gaussian channels. The results show that the authors’ algorithm outperforms the Schmidl Cox's and Park's algorithms in terms of synchronisation error probability and mean square error of timing error.