Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends

Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to connect vast numbers of devices to gather and distribute vital information. Despite individual devices having low energy consumption, the cumulative demand results in significant energy usage. Consequently, the concept of ultra-low-power tags gains appeal. Such tags communicate by reflecting rather than generating the radio frequency (RF) signals by themselves. Thus, these backscatter tags can be low-cost and battery-free. The RF signals can be ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV) signals, or the system can generate them externally. Backscatter channel characteristics are different from conventional point-to-point or cooperative relay channels. These systems are also affected by a strong interference link between the RF source and the tag besides the direct and backscattering links, making signal detection challenging. This paper provides an overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks. It delves into various detection methods, discussing their advantages and drawbacks. The paper's emphasis on signal detection sets it apart and positions it as a valuable resource for IoT and wireless communication professionals and researchers.

Cell-Free Bistatic Backscatter Communication: Channel Estimation, Optimization, and Performance Analysis

This study introduces and investigates the integration of a cell-free architecture with bistatic backscatter communication (BiBC), referred to as cell-free BiBC or distributed access point (AP)-assisted BiBC, which can enable potential applications in future (EH)-based Internet-of-Things (IoT) networks. To that purpose, we first present a pilot-based channel estimation scheme for estimating the direct, cascaded, forward channels of the proposed system setup. We next utilize the channel estimates for designing the optimal beamforming weights at the APs, reflection coefficients at the tags, and reception filters at the reader to maximize the tag sum rate while meeting the tags' minimum energy requirements. Because the proposed maximization problem is non-convex, we propose a solution based on alternative optimization, fractional programming, and Rayleigh quotient techniques. We also quantify the computational complexity of the developed algorithms. Finally, we present extensive numerical results to validate the proposed channel estimation scheme and optimization framework, as well as the performance of the integration of these two technologies. Compared to the random beamforming/combining benchmark, our algorithm yields impressive gains. For example, it achieves $\sim$ 64.8\% and $\sim$ 253.5\% gains in harvested power and tag sum rate, respectively, for 10 dBm with 36 APs and 3 tags.

Adversarial Score-Based Generative Model for AmBC Channel Estimation

This letter presents a pioneering method that employs deep learning within a probabilistic framework for the joint estimation of both direct and cascaded channels in an ambient backscatter (AmBC) network comprising multiple tags. In essence, we leverage an adversarial score-based generative model for training, enabling the acquisition of channel distributions. Subsequently, our channel estimation process involves sampling from the posterior distribution, facilitated by the annealed Langevin sampling (ALS) technique. Notably, our method demonstrates substantial advancements over standard least square (LS) estimation techniques, achieving performance akin to that of the minimum mean square error (MMSE) estimator for the direct channel, and outperforming it for the cascaded channels.

Time-Spread Pilot-Based Channel Estimation for Backscatter Networks

Current backscatter channel estimators employ an inefficient silent pilot transmission protocol, where tags alternate between silent and active states. To enhance performance, we propose a novel approach where tags remain active simultaneously throughout the entire training phase. This enables a one-shot estimation of both the direct and cascaded channels and accommodates various backscatter network configurations. We derive the conditions for optimal pilot sequences and also establish that the minimum variance unbiased (MVU) estimator attains the Cramer-Rao lower bound. Next, we propose new pilot designs to avoid pilot contamination. We then present several linear estimation methods, including least square (LS), scaled LS, and linear minimum mean square error (MMSE), to evaluate the performance of our proposed scheme. We also derive the analytical MMSE estimator using our proposed pilot designs. Furthermore, we adapt our method for cellular-based passive Internet-of-Things (IoT) networks with multiple tags and cellular users. Extensive numerical results and simulations are provided to validate the effectiveness of our approach. Notably, at least 10 dBm and 12 dBm power savings compared to the prior art are achieved when estimating the direct and cascaded channels. These findings underscore the practical benefits and superiority of our proposed technique.

A Design of Low-Projection SCMA Codebooks for Downlink Satellite Internet of Things

This paper presents a systematic investigation on codebook design of sparse code multiple access (SCMA) communication in downlink satellite Internet-of-Things (S-IoT) systems that are generally characterized by Rician fading channels. To serve a huge number of low-end IoT sensors, we aim to develop enhanced SCMA codebooks which enable ultra-low decoding complexity, while achieving good error performance. By analysing the pair-wise probability in Rician fading channels, we deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. To reduce the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). We consider golden angle modulation (GAM), thus the resultant multi-dimensional constellation is called LPGAM. With the proposed design metrics and based on LPGAM, we propose an efficient approach of multi-stage optimization of sparse codebooks. Numerical and simulation results show the superiority of the proposed LP codebooks (LPCBs) in terms of decoding complexity and error rate performance. In particular, some of the proposed LPCBs can reduce the decoding complexity by 97\% compared to the conventional codebooks, and own the largest minimum Euclidean distance among existing codebooks. The proposed LPCBs are available at \url{https://github.com/ethanlq/SCMA-codebook}.