Tensor-Based Receivers for the Bistatic Sensing and Communication Scenario

We propose receivers for bistatic sensing and communication that exploit a tensor modeling of the received signals. We consider a hybrid scenario where the sensing link knows the transmitted data to estimate the target parameters while the communication link operates semi-blindly in a direct data decoding approach without channel knowledge. We show that the signals received at the sensing receiver and communication receiver follow PARATUCK and PARAFAC tensor models, respectively. These models are exploited to obtain accurate estimates of the target parameters (at the sensing receiver) and the transmitted symbols and channels (at the user equipment). We discuss uniqueness conditions and provide some simulation results to evaluate the performance of the proposed receivers. Our experiments show that the sensing parameters are well estimated at moderate signal-to-noise ratio (SNR) while keeping good symbol error rate (SER) and channel normalized mean square error (NMSE) results for the communication link.

Enhanced channel estimation for double RIS-aided MIMO systems using coupled tensor decomposition

In this paper, we consider a double-RIS (D-RIS)-aided flat-fading MIMO system and propose an interference-free channel training and estimation protocol, where the two single-reflection links and the one double-reflection link are estimated separately. Specifically, by using the proposed training protocol, the signal measurements of a particular reflection link can be extracted interference-free from the measurements of the superposition of the three links. We show that some channels are associated with two different components of the received signal. Exploiting the common channels involved in the single and double reflection links while recasting the received signals as tensors, we formulate the coupled tensor-based least square Khatri-Rao factorization (C-KRAFT) algorithm which is a closed-form solution and an enhanced iterative solution with less restrictions on the identifiability constraints, the coupled-alternating least square (C-ALS) algorithm. The C-KRAFT and C-ALS based channel estimation schemes are used to obtain the channel matrices in both single and double reflection links. We show that the proposed coupled tensor decomposition-based channel estimation schemes offer more accurate channel estimates under less restrictive identifiability constraints compared to competing channel estimation methods. Simulation results are provided showing the effectiveness of the proposed algorithms.

Semi-Blind Channel Estimation for Beyond Diagonal RIS

The channel estimation problem has been widely discussed in traditional reconfigurable intelligent surface assisted multiple-input multiple-output. However, solutions for channel estimation adapted to beyond diagonal RIS need further study, and few recent works have been proposed to tackle this problem. Moreover, methods that avoid or minimize the use of pilot sequences are of interest. This work formulates a data-driven (semi-blind) joint channel and symbol estimation algorithm for beyond diagonal RIS that avoids a prior pilot-assisted stage while providing decoupled estimates of the involved communication channels. The proposed receiver builds upon a PARATUCK tensor model for the received signal, from which a trilinear alternating estimation scheme is derived. Preliminary numerical results demonstrate the proposed method's performance for selected system setups. The symbol error rate performance is also compared with that of a linear receiver operating with perfect knowledge of the cascaded channel.

Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER). In this framework, the BS chooses the MCS based on the channel quality indicator (CQI) reported by the user equipment (UE). A transmission is made with the chosen MCS and the results of this transmission are converted by the BS into rewards that the BS uses to learn the suitable mapping from CQI to MCS. Comparing with a conventional fixed look-up table and the outer loop link adaptation, the proposed framework achieves superior performance in terms of spectral efficiency and BLER.