Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

In this paper, we investigate covert communications in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted ambient backscatter communication (AmBC) system with rate-splitting multiple access (RSMA). Specifically, we use STAR-RIS as a backscatter device to enhance the backscatter link against interference from the direct link. In addition STAR-RIS utilizes the RSMA protocol to transmit information to both covert and public users, enabling more efficient use of spectrum resources. In order to evaluate the system performance, we derive expressions for detection error probability (DEP), outage probability (OP), covert rate (CR) and spectral efficiency (SE). Then, we perform an optimal detection threshold analysis for DEP, an asymptotic analysis for OP, as well as a lower bound analysis for CR, and analyze the impact of system parameter changes on the system performance. The numerical results show that the STAR-RIS-RSMA assisted ambient backscatter communication (AmBC) system performs better in terms of the covert performance than the system using tags as well as the system using the non-orthogonal multiple access (NOMA) protocol.

In this paper, we propose multi‐tag selection schemes to improve achievable rate of a secondary ambient backscatter communication (ABC) system with multiple tags and a single power beacon in cognitive radio (CR) environments. Using the secondary power beacon, which plays a role as radio frequency (RF) source when there is no ambient RF source signals, with spectrum sensing capability, the secondary ABC system determines operation modes according to activity of the licensed primary system. When the primary system is active, a tag is randomly selected due to a lack of channel information, while when it is inactive, the best tag maximizing achievable rate is selected using the secondary power beacon. Moreover, we apply a successive interference cancellation (SIC) technique when the secondary power beacon is able to work. The performance of the proposed multi‐tag selection schemes is mathematically analyzed in terms of bit error rate (BER) and achievable rate. Finally, the performance of the proposed schemes is evaluated through simulations in terms of BER and achievable rate. It is shown that our approach can significantly outperform a conventional ABC system in CR environments, especially under a low‐active primary system regime.

In ambient backscatter communication (AmBC), it is a challenging task to recover the tag information at the reader due to the difficulty in obtaining the relevant channel state information (CSI). In this paper, we translate the signal detection problem into a clustering problem, for which two known labels are transmitted from the tag as the prior knowledge to assist clustering initialization and signal detection. By exploiting the received signals directly, two clustering-inspired detection methods are proposed, one is called clustering with labeled signals (CLS), and the other is referred to as clustering with labeled and unlabeled signals (CLUS). Both methods are developed based on the proposed modulation-constrained (MC) Gaussian mixture model (GMM). Finally, extensive simulation results show that the proposed methods only have small gaps compared with the optimal detection with perfect CSI.

In this letter, we investigate a novel ambient backscatter communication (AmBC) system in which an intelligent reflecting surface (IRS) acts as a passive transmitter to communicate with a reader. We are interested in the joint design of the IRS reflecting beamforming and symbol detection to minimize the bit error rate (BER). However, the problem is challenging to be solved optimally, since the BER is related to the clustering-based detector without a concrete close-form expression and the IRS reflecting unit modulus constraint is non-convex. To solve this issue, we propose a novel deep unfolding neural network (DUNN) combining data-driven and model-driven for passive reflecting beamforming design and symbol detection, which is learned to approximate the BER model from the training samples and the unit modulus constraint is satisfied by treating the optimization variables as network parameters. Numerical results demonstrate that the proposed scheme has superior performance of detection.

This paper considers a non-orthogonal multiple access (NOMA)-assisted ambient backscatter communication (AmBC) system. To maximize the achievable sum rate (ASR) of the AmBC system, a joint optimization problem over a backscatter device (BD) grouping strategy, reflection coefficients, and decoding order is formulated, where the BD grouping strategy contains the number of BD groups and the BD allocation strategy. The BD grouping strategy, the reflection coefficients, and the decoding order are all intertwined, and the global search is extremely complex. As a result, we propose a four-step optimization algorithm. First, we give the closed-form optimal solution of the BD decoding order and reflection coefficient for a given grouping strategy. Then, for a given number of BD groups, we propose a low-complexity BD allocation strategy based on the complexity–performance trade-off. Finally, the number of BD groups with the largest ASR is selected as the global optimal number of BD groups. The simulation results show that the proposed four-step optimization algorithm is better than the benchmark solution.

In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

Although the massive machine type communication (mMTC) has not been commercialized fully yet, there is a very attracting ambient backscatter communication system. Based on the backscatter method, the ambient backscatter communication systems use the already existing ambient RF signals such as cellular signal, FM broadcasting signal, WIFI signal, and so on. It is ultra-low power, which is necessary to realize mMTC. However, there is a disadvantage in that it is vulnerable to an interference environment because it uses an ambient RF signal. Therefore, analysis in an environment with interference is necessary. In this paper, we propose a method to analyze the link budget of an ambient backscatter communication system in an environment where interference signals are considered. We propose a method to analyze by adding a loss term that may occur in ambient backscatter, and present the calculation result.

This paper deals with symbol detection in ambient backscatter communication systems with multi-antenna reader. Unlike most of the existing works which assume deterministic ambient radio frequency (RF) signals, we consider another important scenario with complex Gaussian RF signals. Focusing on the on-off keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle, and an exact closed-form expression for the BER is derived. In addition, a simple energy detector is proposed to serve as a performance benchmark. Simulation results show that, implementing multiple antennas at the reader is an effective means to enhance the BER performance. Also, the proposed optimal detector always outperforms the energy detector. Furthermore, unlike the energy detector, whose BER settles in the high signal to noise ratio regime, no error floor exists for the proposed optimal detector.

We consider a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. We propose a scheme to exploit the multiple antenna structure of the system to achieve a lower error-rate performance than conventional ABC systems. Furthermore, we present a novel low complexity energy-based maximum likelihood (EML) GSSK detector, which does not require the perfect knowledge of the ambient source’s signal, unlike the conventional ABC receivers. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression as the number of antennas of the reader becomes large. Finally, we derive a simple, asymptotic PEP at the reader when the noise variance approaches zero, i.e., under the large signal-to-interference ratio regime. We validate our analysis through Monte Carlo simulations and show a small performance loss due to the approximations.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON–OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

In this paper, we first address the effects of the feedback delay on a variable-rate variable-power adaptive modulation MIMO system that is designed under a perfect channel state information (CSI) assumption. Closed-form expressions for the average bit error rate (BER) and average spectral efficiency (ASE) are derived. Based on that, simple but effective adaptive modulation designs using the outdated instantaneous CSI and time correlation coefficient are proposed based on the signal-to-interference and noise ratio (SINK) and BER analysis. Analytical and simulation results show that these designs provide good trade-offs between the ASE and BER in an adaptive way, and make the adaptive MIMO system much more robust to the CSI imperfection

Passive technologies like ambient backscatter communication (AmBC) and intelligent reflecting surface (IRS) hold enormous potential for ubiquitous connectivity of Internet of Things (IoT) devices in future wireless networks. However, despite many research efforts, AmBC has so far been unable to fulfil its potential for widespread deployment, due to the weakness of backscatter signal in the presence of strong direct-link interference from the original RF source. This limits the bit error rate (BER) and hence the transmission rate and range of AmBC systems. Meanwhile, the IRS offers new degrees of freedom in enhancing a variety of systems by transforming their propagation medium and signals. In this work, we devise a novel scheme to improve the detection performance of an AmBC system using an IRS located in its proximity. The IRS augments the backscatter signal quality at the receiver by adjusting its phase shifts to balance signal strengths, ultimately improving the performance of energy detection at the receiver. Our results clearly show that an IRS of reasonable size can considerably improve the BER performance of ambient backscatter, which is an important improvement for low power IoT systems.

Quantitative analysis of residues of chlorpyrifos in corn oil based on Fourier transform near-infrared spectroscopy and deep transfer learning

Ambient backscatter communication (ABC) is a promising technology for future wireless systems due to its ultra-low-power operation and simplicity. To further enhance its performance, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) has been adopted in this work. However, carrier frequency offset (CFO) can disrupt the orthogonality of the MIMO-OFDM signal, which significantly degrades the performance of the reader’s receiver. To address this issue, we consider an asynchronous ABC system and propose a deep learning (DL) algorithm to estimate and compensate the CFO at the reader, ensuring reliable communication in MIMO-OFDM-based ABC systems. The proposed architecture integrates a hybrid attention mechanism, which captures inter-dependencies between the two types of extracted features, and a squeeze-and-excitation network that adaptively refines channel-wise feature responses. The proposed architecture can also work for conventional OFDM and MIMO-OFDM systems. Furthermore, to validate the model’s robustness and generalization capability, additional test datasets are generated for various modulation schemes and different 3GPP TR38.901 tapped delay line channel models. The simulation results indicate its outperformance over existing approaches and has lower computational complexity than the existing methods.

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, the authors propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, the authors compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. The authors show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR.