Backscatter communications, an emerging low-cost and low-consumption green communication paradigm, has received significant attention in both industry and academia. In recent years, multiple-input multiple-output (MIMO) technology has also been integrated into backscatter communications to meet the requirements of transmission performance in IoT scenarios. However, due to the hardware limitations of backscatter tags, one of the main challenges for multi-antenna backscatter tags is to reduce the circuit complexity of space-time block codes (STBC) while maintaining transmission performance. Although low-complexity STBC for dual-antenna tags has been explored, to the best of our knowledge, research on high-performance, low-complexity STBC for four-antenna tags remains in its infancy. In this paper, we propose a 2×4 extended Alamouti code (EAC) with full-rate capability for MIMO backscatter communications. The results show that the required impedance of the proposed EAC on the backscatter tag is less than half of that of the conventional orthogonal STBC (OSTBC), which considerably reduces the complexity of the tag circuit. Additionally, we also derive the asymptotic closed form expression of the symbol error rate (SER) of the proposed EAC to obtain the system insights. The derivation results show that the achievable diversity order of the proposed EAC in 1×4×N backscatter channel is 2×min(2,N), which indicates that the proposed EAC can achieve the same diversity performance as the conventional OSTBC by setting an appropriate number of receiver antennas. Finally, numerical simulations are performed to validate the accuracy of our analysis and the superiority of the proposed EAC in SER.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radios (SRs), an RIS serves not only as a transmitter to deliver its information by reflecting signals from a primary transmitter (PTx) but also as a helper to assist this primary transmission via reconfiguring wireless environments. In this paper, we are interested in an RIS-assisted multiple-input multiple-output (MIMO) SR system, where the RIS has two functions: (1) balancing the channel gains of multiple data streams from the PTx and (2) delivering its own multiple data streams. Considering the passive reflective nature of the RIS, we propose a novel multi-data-stream block delivery scheme, where the reflecting elements of the RIS are segmented into multiple blocks and each block transmits one data stream. The association between the data streams and the corresponding reflecting elements is represented by an indicator matrix. With this information delivery scheme, upper and lower bounds on the sum rate of RIS-assisted MIMO SR are determined and the primary and secondary transmissions’ achievable rates are characterized. Then, we jointly design the transmit covariance matrix at the PTx, the indicator matrix at the RIS, and the passive beamforming vector at the RIS by maximizing the derived upper bound on the sum rate. To show the advantages of RIS-assisted MIMO SR, we examine the traditional precoding matrix-based multi-data-stream delivery scheme of the RIS and explore a special case where the direct link is blocked. Finally, extensive numerical results demonstrate that when the RIS transmits multiple data streams, the system can achieve a higher sum rate compared with the case where the RIS purely enhances the primary transmission, thanks to the additional degree-of-freedom introduced by the RIS transmission.

Backscatter communication has emerged as a promising technology for future Internet of Things (IoT) due to its ultra-low power consumption. However, its performance is limited by the double fading effect in the backscatter channel. In this paper, we incorporate multiple-input multiple-output (MIMO) technique into backscatter communications, where the sinusoidal radio frequency (RF) source, backscatter device (BD), and receiver are all equipped with multiple antennas. Furthermore, cyclic delay diversity (CDD) is employed across antennas at the BD to achieve array gain as well as transmit diversity gain. To cope with the frequency-selectivity introduced by CDD, two frequency-domain signal detection schemes are utilized based on the maximum ratio combining (MRC) and minimum mean square error (MMSE) criteria, and the closed-form expressions for the bit error rates (BERs) of backscatter communications are derived. The transmit beamformer at the RF source is optimized to minimize the BER of the backscatter communication. With properly designed beamforming, the overall backscatter link can be effectively strengthened, thereby enabling more reliable backscatter communications. Simulation results show that deploying multiple antennas at both the RF source and the receiver significantly improves the BER performance, while the CDD-based transmission scheme can help to achieve additional array gain as well as transmit diversity gain for backscatter communications.

Symbiotic radio (SR) has emerged as a promising technology that enables ambient Internet of Things (IoT) devices to passively access wireless networks for data transmission. To support massive low-activity IoT devices, this paper proposes a novel low-activity code-division multiple access (LACDMA) based SR system, where a large number of low-activity backscatter devices (BDs) simultaneously transmit information in a CDMA manner by backscattering signals from a primary transmitter (PTx). To jointly detect the signals from both the PTx and the low-activity BDs, we first develop an optimal sparsity-aware maximum a posteriori (S-MAP) detector, which performs an exhaustive search over all candidates in the transmit alphabet. To address the prohibitive complexity of this approach, we further propose a low-complexity sparsity-aware iterative successive interference cancellation (S-SIC) detector. In this design, the PTx's signal is first decoded by treating the backscatter signals as interference. Then, the BDs' signals are recovered by exploiting their sparse activity patterns with the estimated PTx signal. Subsequently, the PTx's signal is reestimated by regarding the backscatter link as a multi-path with the estimated BDs' signal. Simulation results are provided to evaluate the proposed detectors. It is demonstrated that the developed detectors can realize the multipath gain contributed by the BDs to enhance the primary transmission and the proposed S-SIC detector can closely approach the performance of the SMAP detector, particularly in scenarios with a large number of receive antennas.

In this paper, we investigate a novel symbiotic radio (SR)-aided high-speed railway (HSR) wireless network, in which the Internet of Things (IoT) device, operating as a secondary transmitter, transmits its own information to the mobile relay (MR) on the HSR by backscattering radio frequency (RF) signals from the base station (BS). With the assistance of SR, the designed network facilitates the transmission of locally collected environmental sensing messages from the IoT network to the HSR, simultaneously enhancing the primary communication between the BS and MRs. Aiming to maximize the sum transmission rate of the primary and the IoT network, we focus on a joint power control and device access (JPCDA) problem. Specifically, each IoT device accesses the network through appropriate time slot selection and appropriate power control, thereby achieving satisfactory overall network performance. However, since the fast channel variations arising from the high mobility of HSRs make it impractical to acquire accurate channel state information (CSI), it is challenging to achieve an optimal resource allocation scheme. To address this challenge, we develop a distributed deep reinforcement learning (DRL)-based algorithm that utilizes historical CSI to infer real-time CSI for decision making. In particular, each computing unit of the agent performs action selection for only one IoT device at one time based on the current local observation information. Numerical results illustrate that our proposed algorithm outperforms other baselines, and still works effectively when the environment changes.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflection coefficients. Such RIS functions innately enable a new type of communication channel, called multiplicative multiple access channel (M-MAC), where the primary and secondary signals are superposed in a multiplicative manner. To pursue the fundamental performance limits, in this paper, we focus on characterizing the capacity region for the RIS-assisted SR system. Due to the reflection nature of RISs, the signal transmitted from the RIS elements should satisfy a passive reflection constraint. In particular, we consider two types of passive reflection constraints, one for the case that the amplitudes of the reflection coefficients are fixed but the phases are adjustable, while the other for the case that both the amplitudes and the phases can be adjusted. Under the passive reflection constraints at the RIS as well as the average power constraint at the primary transmitter (PTx), we characterize the capacity region of RIS-assisted SR when the direct link from the PTx to the receiver is blocked. It is observed that: 1) the number of sum-rate-optimal points on the boundary of the capacity region is infinite; 2) for the rate pairs with the maximum sum rate, the optimal amplitude distribution of the primary signal is a continuous Rayleigh distribution, while for the remaining rate pairs on the capacity region boundary, the optimal amplitude distribution of the primary signal is discrete; 3) when both the amplitudes and the phases of the reflection coefficients are adjusted for the RIS, the capacity region is enlarged as compared to the phase-adjusted-only case.

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to meet the rapidly increasing spectrum demand of various applications, because it can significantly improve the spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes, where the beamforming vectors are obtained by optimizing the normalized beamforming vectors and power control. In addition, we consider the case where only statistical channel state information is available and the case where channel estimation errors exist, and propose the corresponding beamforming schemes. Finally, we provide extensive numerical simulations to evaluate the performance of the proposed beamforming schemes and demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.

Cognitive aerial-terrestrial networks (CATNs), which enable aerial and terrestrial networks to share spectrum resources, have been identified as a promising solution to address the spectrum utilization challenges brought by thriving aerial networks. However, during spectrum sharing, aerial users, such as airplanes and flying cars, suffer severe interference from numerous terrestrial base stations (BSs). To alleviate such inter-ference, in this paper, we investigate a coordinated beamforming (CBF) problem, which aims to maximize the sum rate of the secondary terrestrial users while keeping the interference to the primary aerial users below a pre-defined threshold. Since aerial users usually move fast, obtaining real-time global channel state information is challenging. Also, frequently calculating the beamformers with high-complexity algorithms is unaffordable. These issues make traditional iterative optimization algorithms impractical in solving this problem. To address these issues, deep reinforcement learning (DRL) based algorithms can be applied. However, the performance of DRL-based algorithms is sensitive to the penalty weight of a weighted penalty term for violating constraints in the reward function. In this paper, we propose a safe DRL-based distributed dynamic CBF scheme to avoid the tricky adjustment of the penalty weight, where the studied CATN is described as a constrained Markov decision process (CMDP) with cost sets to decouple the objective and constraints. The CMDP is solved by a safe DRL algorithm, which maximizes the reward while satisfying the safety constraints. Simulation results show that the proposed algorithm can achieve a high sum rate of terrestrial users while interference power constraints are generally well satisfied.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radios (SRs), an RIS delivers its information by periodically reflecting the signal from a primary transmitter (PTx) and simultaneously the RIS assists this primary transmission by passive beamforming. In this paper, we are interested in characterizing the multiple-input multiple-output (MIMO) channel capacity for RIS-assisted SR, where both PTx and RIS transmit multiple data streams. To this end, we propose a novel multi-data-stream delivery scheme for the RIS, where the reflecting elements of the RIS are divided into multiple parts and each part is used to transmit one data stream by periodically adjusting its reflection coefficients. With this information delivery scheme, we derive an upper bound on the MIMO channel capacity of an RIS-assisted SR system. To achieve it, we then jointly design the transmit covariance matrix at the PTx and the passive beamforming vector at the RIS by using an alternating optimization algorithm. Considering the high computational complexity of solving the transmit covariance matrix, we further propose two low-complexity algorithms, i.e., upper bound-based algorithm and singular value-based algorithm. Finally, extensive numerical results are presented to show the effectiveness of the proposed algorithms and demonstrate the advantages of the RIS to transmit multiple data streams.

Cognitive spectrum sharing has been regarded as a promising solution to improve spectrum utilization efficiency for space-air-ground integrated networks (SAGINs). However, in SAGIN, different networks may have different quality of service (QoS) requirements, which pose challenges to the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which may not be met when it acts as a secondary network. To address this issue, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGIN, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, in SAGIN, an aerial network and a terrestrial network share the spectrum of a satellite network. HCSSA gives higher priority to the aerial network by a QoS constraint, while the terrestrial network is the ordinary secondary network without protection. Besides, the satellite terminal requires the received interference to be below a threshold. Subject to these two constraints and the maximum transmit power constraints, we aim to maximize the sum rate of the terrestrial network by optimizing the transmit beamforming vectors of the aerial base station (BS) and the terrestrial BSs. To solve this non-convex problem, we propose an iterative beamforming scheme by exploiting the penalty method and the successive convex approximation scheme. Simulation results show the performance of the proposed beamforming scheme and illustrate the advantages of HCSSA compared with the traditional cognitive spectrum sharing architecture.

This paper focuses on a symbiotic radio (SR)-aided high-speed railway (HSR) wireless network, where the base station (BS) in the primary network serves the mobile relays (MRs) on the HSR via orthogonal frequency division multiple access (OFDMA) and the Internet of Things (IoT) devices deployed around the HSR serve as secondary transmitters for information transmission by selecting appropriate time slots. By using the SR technique, the proposed network not only facilitates the transmission of locally collected environmental messages from the IoT network to MRs, but also enhances the primary communications from the BS to MRs. With the aim of maximizing the sum transmission rate of the primary and the IoT network, we formulate a device access problem under time slot allocation constraints. However, the time-varying channel due to the high mobility of HSR makes it challenging to obtain an optimal policy for the problem. To overcome this challenge, we develop a distributed deep reinforcement learning (DRL) algorithm, which utilizes historical knowledge to infer real-time information to make decisions. Particularly, the proposed algorithm performs action selection for only one IoT device at one time based on the current local observation information. Numerical results demonstrate that the performance of the proposed distributed DRL algorithm closely approximates the optimal strategy that requires perfect instantaneous information.

The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of communication links in wireless networks. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. Energy-intensive blockchain functions can quickly drain the power of nodes, thus degrading consensus performance. Fortunately, a symbiotic radio (SR) system enabled by cognitive backscatter communications can solve the above problems. In SR, the secondary transmitter (STx) transmits messages over the radio frequency (RF) signal emitted from a primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. Such an approach is useful for almost all vote-based CMs, such as the Practical Byzantine Fault-tolerant (PBFT)-like and the RAFT-like CMs. This paper proposes symbiotic blockchain consensus (SBC) by transforming 6 PBFT-like and 4 RAFT-like state-of-the-art (SOTA) CMs to demonstrate universality. These new CMs will benefit from mutualistic transmission relationships in SR, making full use of the limited spectrum resources in WBN. Simulation results show that SBC can increase the consensus success rate of PBFT-like and RAFT- like by 54.1% and 5.8%, respectively, and reduce energy consumption by 9.2% and 23.7%, respectively.

Symbiotic radio (SR) is a promising solution to achieve high spectrum- and energy-efficiency due to its spectrum sharing and low-power consumption properties, in which the secondary system achieves data transmissions by backscattering the signal originating from the primary system. In this paper, we are interested in the pilot design and signal detection when the primary transmission adopts orthogonal frequency division multiplexing (OFDM). In particular, to preserve the channel orthogonality among the OFDM sub-carriers, each secondary symbol is designed to span an entire OFDM symbol. The comb-type pilot structure is employed by the primary transmission, while the preamble pilot structure is used by the secondary transmission. With the designed pilot structures, the primary signal can be detected via the conventional methods by treating the secondary signal as a part of the composite channel, i.e., the effective channel of the primary transmission. Furthermore, the secondary signal can be extracted from the estimated composite channel with the help of the detected primary signal. The bit error rate (BER) performance with both perfect and estimated CSI, the diversity orders of the primary and secondary transmissions, and the sensitivity to symbol synchronization error are analyzed. Simulation results show that the performance of the primary transmission is enhanced thanks to the backscatter link established by the secondary transmission. More importantly, even without the direct link, the primary and secondary transmissions can be supported via only the backscatter link.

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), the RIS acts as a secondary transmitter by modulating its information bits over the incident primary signal and simultaneously assists the primary transmission, then a cooperative receiver is used to jointly decode the primary and secondary signals. Most existing works of SR focus on using RIS to enhance the reflecting link while ignoring the ambiguity problem for the joint detection caused by the multiplication relationship of the primary and secondary signals. Particularly, in case of a blocked direct link, joint detection will suffer from severe performance loss due to the ambiguity, when using the conventional on-off keying and binary phase shift keying modulation schemes for RIS. To address this issue, we propose a novel modulation scheme for RIS-assisted SR that divides the phase-shift matrix into two components: the symbol-invariant and symbol-varying components, which are used to assist the primary transmission and carry the secondary signal, respectively. To design these two components, we focus on the detection of the composite signal formed by the primary and secondary signals, through which a problem of minimizing the bit error rate (BER) of the composite signal is formulated to improve both the BER performance of the primary and secondary ones. By solving the problem, we derive the closed-form solution of the optimal symbol-invariant and symbol-varying components, which is related to the channel strength ratio of the direct link to the reflecting link. Moreover, theoretical BER performance is analyzed. Finally, simulation results show the superiority of the proposed modulation scheme over its conventional counterpart.

This paper studies the reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS acts as a secondary transmitter to transmit its information by leveraging the primary signal as its RF carrier and simultaneously assists the primary transmission. Conventionally, all reflecting elements of the RIS are used to transmit the secondary signal, which, however, would limit its capability for assisting the primary transmission. To address this issue, we propose a novel RIS partitioning scheme, where the RIS is partitioned into two sub-surfaces, one to assist the primary transmission and the other to transmit the secondary signal. Naturally, there exists a fundamental tradeoff between the assistance and transmission capabilities of RIS regarding the surface partitioning strategy. Considering the coupling effect between the primary and secondary transmissions, we focus on the detection of the composite signal formed by the primary and secondary ones, based on which we propose a novel two-step detector. Then, we formulate the assistance-transmission tradeoff problem to minimize the bit error rate (BER) of the composite signal by jointly optimizing the surface partitioning strategy and the phase shifts of the two sub-surfaces, such that the overall BER of RIS-assisted SR is minimized. By solving this problem, we show that the optimized surface partitioning strategy depends on the channel strength ratio of the direct link to the reflected link. Moreover, performance analysis shows that when the direct link is blocked, exchanging the number of reflecting elements used for assistance and transmission can still achieve almost the same BER of the composite signal thanks to the coupling effect. Finally, extensive simulations show that our proposed RIS partitioning scheme outperforms the conventional schemes which use all reflecting elements for either assistance or transmission.

Wireless blockchain networks have played an important role in many network scenarios, among which wireless Practical Byzantine Fault Tolerance (PBFT) consensus is regarded as one of the most important consensus mechanisms. It enables nodes in wireless networks to reach consistency without any trusted entity. However, due to the instability of wireless communication links, the reliability of the PBFT consensus will be seriously affected. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. The high-energy-consumption blockchain functions will quickly consume the power of nodes, thus, affecting consensus performance. Fortunately, the symbiotic radio (SR) system enabled by cognitive backscatter communications can provide a solution to the above problems. In SR, the secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signal of the primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. In our paper, we propose the symbiotic PBFT (S-PBFT) consensus benefited from the mutualistic transmission in SR, which can increase the consensus security by 54.82 %, and save energy consumption by about 10%.

In RIS-assisted symbiotic radio (SR) systems, the RIS transmits messages by using the spectrum and energy of the primary transmission and simultaneously assists the primary transmission by passive beamforming. Due to the functionality of information transmission, the RIS could not always enhance the primary transmission. Instead, We observe that the performance of the primary transmission in terms of bit error rate (BER) will drop first and then improve with the increase in the number of reflecting elements of the RIS, i.e.,$K$. Motivated by this, in this paper, we are interested in a mutualistic condition on$K$for RIS-assisted SR, through which, the primary transmission could obtain the performance gain from the RIS. First, we design a transmission scheme for the RIS, which uses the different phase shifts to represent different transmission bits. Then, we optimize the phase shifts of the RIS, based on which, we derive an upper bound on BER for both primary and secondary transmissions. Compared with the BER performance in the absence of the RIS, we derive the mutualistic condition on the number of the reflecting elements$K$. Finally, simulation results are provided to verify the effectiveness of the theoretical analysis.

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, the RIS transmits information by modulating its information bits over RF signals from a primary transmitter (PTx), and simultaneously, the RIS assists the primary transmission by passive beamforming. Considering the inevitable channel estimation errors arising in practice, in this paper, we are interested in quantifying the effects of imperfect knowledge of channels on the channel capacity for both primary and secondary transmissions in RIS-assisted SR. For the primary transmission, we first derive upper and lower bounds on the achievable rate with channel estimation errors. Based on the derived lower bound, we investigate the minimum number of reflecting elements of an RIS that can enable the performance enhancement of the primary transmission compared to the case without the RIS. For the secondary transmission, exact and asymptotic achievable rates are derived. Finally, extensive numerical results are presented to demonstrate the effects of the channel estimation errors together with the interrelationship between primary and secondary transmissions.

In this paper, we are interested in reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, where an RIS assists a primary transmission by passive beam-forming and simultaneously acts as a secondary transmitter to modulate its own information by periodically adjusting its reflecting coefficients. The above modulation scheme innately enables a new multiplicative multiple access channel (M-MAC), in which the primary and secondary signals are superposed in a multiplicative and additive manner. To pursue the fundamental performance limits of the M-MAC, we focus on the characterization of the capacity region of such systems. Due to the passive nature of RISs, the transmitted signal of the RIS should satisfy the peak power constraint. Under this constraint at the RIS as well as the average power constraint at the primary transmitter (PTx), we analyze the capacity-achieving distributions of the transmitted signals and the optimal reflecting coefficients of the RIS. Then, we derive the maximum achievable rates for both primary and secondary transmissions and characterize the rate region of the M-MAC. It is observed that the secondary transmission can achieve the maximum rate when the PTx transmits signals with the constant envelope. Furthermore, the rate region of the M - MAC is strictly convex and larger than that of the conventional TDMA scheme.

This paper considers the reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, in which the RIS, acting as a secondary transmitter (STx), transmits its information by leveraging the primary signal as its RF carrier and simultaneously assists the primary transmission. In such a system, when the RIS transmits information, it could only provide limited assistance for the primary transmission. Thus, there exists a fundamental tradeoff between the assistance and information transmission capabilities of the RIS. To study the tradeoff, in this paper, we propose a novel RIS design scheme which partitions the RIS surface into two sub-surfaces, one to assist the primary transmission and the other to transmit the secondary signal. Considering the coupling effect between these two transmissions in SR, we focus on the composite signal formed by the primary and secondary signals. Then, to optimize the surface partitioning strategy, we formulate the assistance-transmission tradeoff problem to minimize the bit error rate (BER) of the composite signal, so as to improve the overall BER performance of the primary and secondary signals. By solving the problem, we show that optimal surface partitioning is related to the strength ratio of the direct link to the reflected link. Finally, simulation results show that the proposed design outperforms the conventional design significantly, which provides the best tradeoff.

In this paper, we are interested in a simultaneously transmitting and reflecting RIS (STAR-RIS)-assisted SR system, where all elements of a STAR-RIS are divided into two groups, one operating in the reflection mode to enhance the primary transmission, and the other operating in the transmission mode to transmit its own information bits. For such a system, we aim to jointly design the receiver and the phase shifts of the STAR-RIS. To avoid the ambiguity problem caused by the multiplication feature of SR, we employ ASK and PSK modulation schemes at the primary transmitter and the STAR-RIS, respectively. With such modulation schemes, we design the signal detection schemes for both primary and secondary transmissions and then analyze their corresponding symbol error rate (SER) performance. To prioritize the QoS of the primary transmission and simultaneously minimize the SER of the secondary transmission, we formulate one optimization problem to optimize the mode-switching scheme and phase shifts design scheme for STAR-RIS. Due to the non-convexity of the formulated problem, we employ the penalty method together with the successive convex approximation (SCA) method to iteratively solve it. Finally, extensive simulation results are presented to demonstrate the advantages of the STAR-RIS-assisted SR system and the effectiveness of the joint phase shifts and receiver design scheme.

Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver and multiple IoT devices simultaneously transmit their messages by passively backscat-tering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information to the receiver. The receiver jointly decodes the information from the BS and the IoT devices. To evaluate the above two schemes, we derive the closed-form expressions of the ergodic rates for both schemes. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed two multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce computational complexity while achieving equivalent performance to the SA scheme.

Symbiotic radio (SR) is a promising solution to achieve high spectrum- and energy-efficiency due to its spectrum sharing and low-power consumption properties, in which the secondary system achieves its data transmission by backscattering the signal originating from the primary system. In this paper, we are interested in the pilot design and signal detection when the primary transmission adopts orthogonal frequency division multiplexing (OFDM) scheme. In particular, in order to preserve the channel orthogonality among the OFDM sub-carriers, each secondary symbol is designed to span one OFDM symbol. The comb-type pilot is employed by the primary transmission, while the preamble pilot is used by the secondary transmission. With the designed pilot structures, the primary signal can be detected via the conventional methods by treating the secondary signal as a part of the composite channel. Furthermore, the secondary signal can be extracted from the estimated composite channel with the help of the detected primary signal. The bit error rate (BER) performance of the primary and secondary transmissions with both perfect and estimated CSI is analyzed. Simulation results show that the performance of the primary transmission is enhanced thanks to the backscatter link established by the secondary transmission. More importantly, even without the direct link, the primary and secondary transmissions can be supported via only the backscatter link.

In symbiotic radio (SR), the secondary transmitter (STx) modulates its information over the RF signal from the primary transmitter (PTx). This modulation technology, also called “modulation in the air”, leads to the multiplication of the primary and secondary signals. Thus, SR can be modeled as a multiplicative multiple access channel (M-MAC). In this paper, we propose a modulation scheme for such an M-MAC, which consists of two additive parts: the symbol-invariant component used to aid the primary transmission and the symbol-varying component used to deliver STx information. By optimizing these two components, we can strike a balance between the primary and secondary transmissions. Particularly, due to the coupling between these two transmissions in the M-MAC, we focus on the composite signal formed by the primary and secondary signals. Then, we optimize the above two components by maximizing the minimum Euclidean distance as well as minimizing the Hamming distance between the adjacent constellations of the composite signal. Furthermore, we derive the closed-form solution of the optimal modulation scheme, which is related to the ratio of the direct link to the backscatter link. Finally, simulation results are provided to verify the effectiveness of our proposed scheme.

In a backscatter communication system, the backscatter device (BD) transmits its messages to the backscatter receiver (BR) by reflecting the incident signal from an external radio frequency (RF) emitter, instead of using power-hungry active RF components themselves. Thus, backscatter communication has shown great potential for achieving low-power communication. The double-fading effect associated with the backscatter link, however, is a major limiting factor to achieve efficient backscatter communication. Reconfigurable intelligent surfaces (RISs), a recently developed technology, can be applied at the BD to enhance the backscatter link thanks to the fact that both RIS and backscatter communication share the same reflective principle. Such a design can also allow the backscatter communication system to capture the desired RF signal as a reflective carrier in a complex radio environment. In this article, a comprehensive overview of backscatter communication assisted by RIS is given. We first introduce the basics of backscatter communication, which covers the antenna scattering principle, backscatter modulation, and link budget calculation. Then, the details of RIS are discussed, which include antenna-based RIS and metamaterial-based RIS, followed by the discussion of the roles of RIS in backscatter communication. After that, we provide an overview of three types of backscatter communication systems assisted by RIS, including RIS-assisted unmodulated backscatter communication, RIS-assisted ambient backscatter communication, and RIS-assisted symbiotic radio. Emerging applications of these systems, technical challenges, and future opportunities in this emerging field are also presented.

In symbiotic radio (SR), a secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signals received from a primary transmitter (PTx), and in return, the secondary transmission provides multipath gain to the primary transmission. In this paper, we are interested in the fundamental mutualistic mechanism between the primary and secondary transmissions, which describes the condition through which the two systems can benefit each other. Since the symbol period ratio$K$between secondary and primary transmissions is an important system parameter that affects the mutualistic symbiosis, we first derive the theoretical performance in terms of bit error rate (BER) for both primary and secondary transmissions for arbitrary$K$by using QPSK modulation scheme at the PTx and BPSK modulation scheme at the STx as an example setup. Then we the obtain closed-form expressions for the condition on$K$to enable mutualistic symbiosis in SR, which is not related to the specific channel realizations but determined by the average strengths of the direct and backscatter links when the number of receiving antennas is large. Meanwhile, we analyze the average BER performance and the diversity orders for both transmissions in the high signal-to-noise-ratio (SNR) regime. Extensive simulations and numerical results are provided to verify the accuracy of our theoretical analysis and demonstrate the interrelationship between the primary and secondary transmissions.

In symbiotic radio (SR), also called cognitive backscatter communications, a secondary transmitter (STx) transmits messages by modulating its information over the RF signals from a primary transmitter (PTx), and in return, the secondary transmission provides multipath gain instead of interference to the primary transmission when the spreading factor$K$of SR is large enough. In this paper, we are interested in the fundamental mutualistic mechanism between the primary and secondary transmissions in SR, which describes the condition through which the two systems can benefit each other. We first derive the closed-form expressions for the bit error rates (BERs) for both primary and secondary transmissions for general$K$, then obtain the condition on$K$to enable mutualistic symbiosis in SR. It is observed that the critical point of$K$is related to the average strengths of the direct and backscatter links when the number of receiving antennas is large. Extensive simulation and numerical results are provided to verify the accuracy of theoretical analysis and demonstrate the interrelationship between primary and secondary transmissions.

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system.

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from O(N2) to O(1), which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

In the overlay D2D networks, multiple D2D pairs coexist with full frequency reuse resulting in complicated interference. Traditional centralized power control methods require instantaneous interference information and so are difficult to implement in a D2D network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core idea of the scheme is described as follows. First, we design linear filters with various sizes termed IFEs to extract the local interference patterns from the outdated interference information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, and so as to provide more effective power allocation strategies. Then, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network (RNN). The acquisition of the channel correlation can greatly improve the efficiency and the accuracy of our network according to our simulation results. It is worth noting that an input reduction process is also designed to reduce the space complexity from O(N2) to O(1) which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

To overcome the challenges in achieving extremely high throughput and super-massive access in future wireless communications, in this paper, we focus on a novel large intelligent surfacelantennas (LISA)-assisted symbiotic radio (SR) system. Specifically, in the proposed system, a LISA transmits messages to its destination by using backscatter communication, and at the same time, it assists the transmission from a base station (BS) to its user by intelligently reconfiguring the wireless environment. In this paper, we are interested in the joint active (BS) and passive (LISA) beamforming design problem to maximize the transmission rate of LISA subject to the BS transmission rate constraint. Due to the non-convexity of the problem, we first relax the rank-one constraint based on the technique of semi-definite relaxation (SDR) and then decouple that optimization problem into two subproblems based on the block coordinate descent (BCD) method, each of which is a convex problem. Due to the expectation terms in the constraints, we propose two algorithms called the Lagrangian algorithm and approximate algorithm to address it. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system.

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, and infrastructure-efficient communications. Each IoT device transmits its own information by backscattering the signals from the primary network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis between the cellular network and the IoT network and focus on the user association problem in SRN. Specifically, the base station (BS) in the primary network serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use historical information to infer the current information in order to make appropriate decisions. One algorithm, referred to as centralized DRL, makes decisions for all IoT devices at one time with globally available information. The other algorithm, referred to as distributed DRL, makes a decision only for one IoT device at one time using locally available information. Finally, simulation results show that the two proposed DRL algorithms achieve performance comparable to the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.

In this paper, we are interested in symbiotic radio networks (SRNs) and focus on the user association problem in SRNs. Specifically, in an SRN, the base station serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem.To overcome this issue, we propose a deep reinforcement learning (DRL) algorithm, which uses historical knowledge to infer the current information in order to make appropriate decisions for this user association problem. Finally, simulation results show that the proposed DRL algorithm achieves performance comparable to the optimal user association policy which requires perfect real-time information.

Non-orthogonal multiple access (NOMA) is envisioned to be a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication (AmBC) is considered as a promising solution to Internet-of-Things (IoT), due to its high spectrum- and power-efficiency. In this paper, we are interested in an integrated system of cellular and IoT networks, and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with backscatter devices. In this system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a backscatter device transmits its information over the BS signals to one cellular user using passive radio technology. We derive the closed-form expressions of the outage probabilities and analyze the diversity orders of all relevant transmissions in the proposed system. Finally, we provide numerical results to verify the theoretical analysis.

Cognitive ambient backscatter communication is a novel spectrum sharing paradigm, in which the backscatter system shares not only the same spectrum, but also the same radio-frequency source with the legacy system. Conventional energy detector (ED) suffers from severe error floor problem due to the existence of co-channel direct link interference (DLI) from the legacy system. In this paper, novel error-floor-free detectors are proposed to tackle the DLI using multiple receive antennas at the reader. First, beamforming-assisted ED and likelihood-ratio-based detector are proposed for backscatter symbol detection when the reader has perfect channel state information (CSI). Then a novel statistical clustering framework is proposed for joint CSI feature learning and backscatter symbol detection. Extensive simulation results have shown that the proposed methods can significantly outperform the conventional ED. In addition, the proposed clustering-based methods perform comparably as their counterparts with perfect CSI.

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.

Cognitive ambient backscatter communication (AmBC) is a novel spectrum sharing paradigm for green Internet of Things, in which the backscatter system shares not only the same spectrum, but also the same radio- frequency (RF) source with the legacy system. The conventional energy detector (ED) suffers from a severe error floor problem due to the existence of co-channel direct link interference (DLI) from the legacy system. In this paper, novel error-floor-free detectors are proposed to tackle the DLI through multi-antenna receive beamforming. A novel statistical clustering framework is proposed for joint channel state information (CSI) learning and backscatter symbol detection. Extensive simulation results have shown that the proposed methods can significantly outperform the conventional ED, and verified that the proposed clustering-based method achieves performance comparable to that of the perfect CSI cases.

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.

Inspired by the phenomenon that the received signals naturally form clusters, we propose a novel machine learning framework to design multi-input multi-output (MIMO) communication systems. In the proposed framework, the MIMO detection problem is converted into a clustering problem, and known labels are transmitted to assist the receiver for labeling the clusters. A modulation-constrained Gaussian mixture model (MC-GMM) and the associated optimization algorithm are developed to reduce the number of parameters to be learnt in the clustering algorithm. Furthermore, we propose a method called label reconstruction to minimize the overhead of label transmission, and the design of the optimal labels is studied. Simulation results are presented to verify the effectiveness of the proposed label-assisted clustering (LAC) receiver in approaching the optimal maximum likelihood detection (MLD) with perfectly known channel knowledge for typical MIMO systems.

Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.

In ambient backscatter communications (Am-BC), a backscatter device can harvest power from ambient RF signals and modulate its information symbols over the ambient carriers without using complex RF transmitter. Conventional receiver design for AmBC focuses on tackling the direct link interference from the RF source. In this paper, a novel receiver, which is called cooperative receiver, is proposed to recover signals not only from the ambient backscatter device (A-BD), but also from the RF source. We first study the optimal maximum-likelihood (ML) detection for such system. Then, by exploiting the structural property of the system model, linear detectors and successive interference cancellation (SIC) based detectors are proposed. We also derive the closed-form bit error rate (BER) expressions for both ML detection and the proposed SIC algorithms. Finally, extensive numerical results show that the existence of backscattered signal in the considered system can significantly enhance the ML detection performance of the source signal, and the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios.