Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks

The basic circuit for backscattering radios is shown in Fig. 2. Suppose that $s(t)$ is baseband signal transmitted from PTx, the RF output from the PTx antenna is $x_{0}(t)={\rm{Re}}\{\sqrt{p}s(t)e^{j2\pi f_{c}t}\}$, where $f_{c}$ is the carrier frequency. Let $x(t)$ be the input to the backscattering circuit at STx, and $\theta(t)$ be the reflection coefficient, then signal reflected from the backscattering circuit is $\theta(t)x(t)$.

Let $Z_{a}$ and $Z_{L,i}$ be the antenna and load impedances of the backscattering circuits at STx, respectively. From the antenna scattering theory [36], the electric field of the reflected signal can be decomposed into two components, the structural mode backscattering component, and the antenna mode backscattering component. The structural mode backscattering component can be accounted into part of the environmental multipath [37], while the antenna mode backscattering component is determined by the mismatch between the antenna and load impedances, yielding the reflection coefficient given by

Note when $Z_{L,i}=Z_{a}^{*}$, $\Gamma_{i}=0$. Thus, by changing the value of the load impedance with a switcher, the STx can generate the following time-varying reflection coefficient

if load $Z_{L,i}$ is switched on at time instant $t$.

By changing the load impedance periodically, the STx can generate different reflection coefficients, which represent the information to be sent to the SRx. The key issue for backscattering modulation is to design an appropriate reflection coefficient set corresponding to the signal constellation set $\mathcal{A}_{\sf c}$. Basically, the number of the load impedance states attributes to the modulation order. Taking binary phase shift keying (BPSK) as an example, the STx only needs two load impedance states to represent symbols ’-1’ and ’1’. In order to improve the transmission efficiency, high order modulation schemes, like quadrature amplitude modulation (QAM), can be developed. The relation between the complex constellation point $c_{i}\in\mathcal{A}_{\sf c}$ and the reflection coefficient $\Gamma_{i}$ is given by [38]

where $\alpha$ accounts for the reflection efficiency of the circuits [10] and its value is generally $0\leq\alpha\leq 1$ with the passive load, and $|c|_{max}=\max_{c\in\mathcal{A}_{\sf c}}|c|$ is the largest amplitude of the constellation points, which is presented in the denominator due to the limitation that the passive reflection coefficient cannot be greater than unity. Without loss of generality, we normalize the constellation points such that $|c|_{max}=1$. Once the reflection coefficient $\Gamma_{i}$ is given, the corresponding load impedance $Z_{L,i}$ is chosen as

From Fig. (a), the backscattering link suffers from double fading, and thus the strength of the backscattering link signal in SR is much weaker than that of the direct link signal, which will limit the performance of the secondary system and the improvement to the primary system. Typically, there are two solutions to enhance the strength of the backscattering link. One is to amplify the backscattering link by using an active load at the STx. The other is to introduce multiple antenna elements or called RIS at the STx. The use of RIS in STx can not only enhance the backscattering link, but also capture the RF signals from desired sources using passive beamforming, and its details will be discussed in Section VI. Here, we mainly describe the amplification principle based on the backscattering technology.

Recent advances in backscattering communications show that the STx can take advantages of the active load, whose resistance is negative, to amplify the incident signal [39]. Specifically, provided that the impedance of the active load is $Z_{L,i}=-R_{L,i}+jX_{L,i},\quad R_{L,i}>0$, and the antenna impedance is $Z_{A}=R_{A}+jX_{A},~{}R_{A}>0$, the reflection coefficient is then characterized with

which implies that the STx can amplify the incident signal. Such signal amplification however needs additional biasing source to support the active load, which can be realized by Tunnel diodes [40, 39, 41, 42] or CMOS technology [43].

Denote $T_{s}$ and $T_{c}$ as the symbol periods for the primary transmission and the secondary transmission, respectively. In SR, we assume that each secondary symbol period covers $K$ ($K$ is an integer) primary symbol periods, i.e., $T_{c}=KT_{s}$, as shown in Fig. 3. Denote $c(n)\in\mathcal{A}_{\sf c}$ as the $n$th symbol of the secondary transmission and $s_{k}(n)\in\mathcal{A}_{\sf s}$ the $k$th primary symbol within the $n$th secondary symbol period, where $k=1,\cdots,K$. Note that synchronization between $s_{k}(n)$ and $c(n)$ is required for $K=1$ to avoid spectrum growth, but for large $K$, the spectrum growth due to asynchronous transmission of $s_{k}(n)$ and $c(n)$ becomes negligible [44, 45].

We assume that the SRx has $M_{r}$ antennas and the channel remains unchanged during a secondary data frame but may vary from one frame to another. Denote by $h_{m,1}$ the channel coefficient from the PTx to the $m$-th receive antenna at the SRx, by $l$ the channel coefficient from the PTx to the STx, and by $g_{m}$ the channel coefficient from the STx to the $m$-th receive antenna at the SRx. Let $p$ be the average transmit power at the PTx. The received signal at the SRx can be written as

for $k=1,\cdots,K$, where $\mathbf{h}_{1}=[h_{1,1},\cdots,h_{M_{r},1}]^{T}$, $\mathbf{g}=[g_{1},\cdots,g_{M_{r}}]^{T}$, $\mathbf{h}_{2}=\alpha l\mathbf{g}$, and $\mathbf{u}_{k}(n)\sim\mathcal{CN}(0,\sigma{{}^{2}}\mathbf{I})$. In (6), the first and second terms of the right hand side represent the direct link and backscattering link components, respectively.

Similarly, the received signal at the PRx can be written as

for $k=1,\cdots,K$, where $\mathbf{f}_{1}$ is the channel information from the PTx to the PRx, $\mathbf{q}$ is the channel information from the STx to the PRx, $\mathbf{f}_{2}=\alpha l\mathbf{q}$, and $\mathbf{v}_{k}(n)\sim\mathcal{CN}(0,\sigma{{}^{2}}\mathbf{I})$. Due to the symmetry of the transmission structure in (6) and (7), we consider the signal detection at SRx only. The extension to the signal detection at PRx is straightforward.

When pilot symbols for both primary and secondary transmissions are jointly designed and transmitted, the channel responses $\mathbf{h}_{1}$ and $\mathbf{h}_{2}$ can be estimated at the SRx. Coherent receivers [25], such as optimal maximum-likelihood (ML) detector, linear detector, and successive interference cancellation (SIC)-based detector, can be designed to jointly recover the primary and secondary signals.

When the SRx knows the STx pilots only and partial knowledge about the primary transmission is available, semi-blind receiver can be designed to recover the secondary message. Particularly, machine-learning detectors have been designed to recover the secondary message by using clustering frameworks [46]. The constellation learning-based signal detection scheme uses the constellation characteristic of the primary symbol to decode $c(n)$. The received signals, $\mathbf{y}_{k}(n)$, naturally fall into clusters, which can be divided into two groups, corresponding to the secondary symbols “-1” or “1”. Thus, only two secondary labels are sent before secondary data transmission to map the two groups into the secondary symbols. It is worth noting that the cluster centroids can be represented by a small number of parameters, which can be learned using small sets of data. In addition, blind classification methods can be used to by SRx to recognize the modulation scheme of the primary system [47].

In [48], classification via $k$-nearest neighbors is used to recover the STx symbols. The strong direct link signal is firstly eliminated through projecting the received signal at the SRx into its orthogonal space using the estimated direction-of-arrival (DoA) information of the direct link signal. Then proper beamformer at the SRx is designed to construct a test statistic, which is classified to recover the transmitted secondary symbol by using the $k$-nearest neighbors classification algorithm.

CR is an enabling technology to support efficient utilization of the radio spectrum, in which the secondary user is allowed to reuse the frequency bands assigned to the licensed primary user in an opportunistic or spectrum sharing manner [4, 5, 6, 7].

For the opportunistic manner of CR, the secondary user carries out spectrum sensing to detect the spectrum holes where the assigned frequency bands are not being utilized at a particular time by the primary user. After sensing the spectrum hole, the STx transmits messages to the SRx on the identified spectrum hole and simultaneously frequently monitors the operating spectrum to ensure that the PTx is not active. Once the PTx is active, the secondary user needs to vacate the operating spectrum for the primary transmission. The the activity model of the primary users can be used to assist for such operation [49]. Such model can be used to describe the coexistence between LTE and WiFi spectrum sharing system [50].

For the spectrum sharing manner of CR, as shown in Fig. 1(b), the secondary transmission coexists with the primary transmission at the same time, as long as the caused interference to the primary transmission is below a tolerable threshold. To calculate the caused interference at the the PRx, the STx is required to predict the channel information from the STx to the PRx. Moreover, the STx needs to know the tolerable threshold at the PRx to protect the primary transmission. With the above knowledge, power control is conducted at the STx to guarantee that there is no excessive interference to the PRx. The power control problem under either additive white Gaussian noise (AWGN) or fading channels with either the peak or the average interference power constraints are studied in [51, 52, 53]. The iterative water filling algorithm is commonly used to solve the optimal power allocation problem, in which the large gain channels are allocated with high power while the small gain channels are allocated with low power or no power.

Nevertheless, the primary and secondary transmissions in spectrum sharing model always interfere with each other, which limits the enhancement of the spectrum efficiency for CR. The secondary transmission in SR however can benefit the primary system due to the introduction of the multipath diversity. On the other hand, both PTx and STx in CR are with power-consuming active RF chains while SR provides power-dimensional sharing and infrastructure-dimensional sharing through backscattering radios in addition to the spectrum sharing. Therefore, compared with the CR technology, the SR technology can achieve mutual benefits and multiple dimensional resource sharing with high spectrum-, energy- and cost-efficiency.

In AmBC, the primary and secondary systems are independently designed, thus the secondary system has no information about the primary system, such as modulation scheme, pilots, frame structure, etc. As such, the channel state information is difficult to acquire and noncoherent detection, such as energy detection that just classifies the average received energy into several categories, is commonly used to recover the secondary symbols [9, 12, 13, 14, 15, 16, 54].

In [9], the differential coding is used at the STx to avoid the channel estimation. The SRx firstly detects the STx transmitted symbols with energy detector and then uses the differential decoding to recover the transmitted information. The BER performance of the energy detector with differential coding under the signal-antenna PRx is analyzed in [12]. In [13], the joint energy detection is used to recover the STx information, in which the differential coding characteristics is combined into the energy detector for better performance. In [14], the pilots are transmitted at the STx to assist symbol recovery based on energy detection instead of using differential coding. To achieve high data rate of the secondary transmission, the high-order modulation, $M$-PSK, is employed for backscattering at the STx in [15] and the optimal multilevel energy detector is used to recover the STx messages. Moreover, in [16], the STx transmits information by adjusting three states: positive and negative phase backscatter, and non-backscatter. Then, the corresponding detector at the SRx is designed to extract STx information. Manchester coding and differential Manchester coding are adopted at the STx in [54] to perform reliable detection and the corresponding detectors are proposed to recover the STx information.

Despite the above notable receiver design for AmBC, the energy detector suffers from a large performance loss since it treats the direct link signal as undesired interference. There are some proposals to avoid the direct link interference or to cancel out such interference. Specifically, in [17, 18, 19], the STx shifts the primary signal to an adjacent non-overlapping frequency band to avoid the direct link interference while this technique produces two sidebands, which may interfere with other communications. Waveform design at the STx is applied in [21, 20, 22, 23] to cancel out the direct link interference by exploiting the cyclic prefix structure when the PTx transmits orthogonal frequency division multiplexing (OFDM) signals.

While the proposals to avoid the direct link interference or to cancel out such interference can enhance the receiver performance, the non-coherent nature of the receivers makes it difficult for AmBC to achieve highly reliable backscattering communications. However, in SR, the primary and secondary systems work in collaborative manner, through which not only the PTx and the STx can be jointly designed, but also each receiver can jointly decode the primary and secondary messages. As a result, the existence of secondary transmission in SR can in return enhance the performance of the primary system.

To conclude, the detailed comparison among CR, AmBC, and SR is summarized in Table II.

From (7), the primary symbol, $s_{k}(n)$, is in both the direct and backscattering links, thus the receiver can treat the backscattering link as an additional path when decoding the primary symbols. As such, the achievable rate $R_{s}$ of the primary system satisfies

This upper bound is achieved when $c(n)$ is perfectly decoded. For large $K$, such perfect decoding for $c(n)$ becomes possible due to the spreading gain. When $K=1$, however, it is a challenging task to perfectly decode the secondary signal $c(n)$ at the receiver. In this case, a worst situation happens when the backscattering link signal is thoroughly treated as interference when decoding the primary symbols. Thus we have

For the secondary system, $s_{k}(n)$ is firstly decoded and then the SRx cancels the interference from the direct link signal. Thus when decoding $c(n)$, the primary signal $s_{k}(n)$ can be viewed as a spread-spectrum code with length $K$ [21]. For $K=1$, the achievable rate of the secondary system is given by [31]

For large $K$, the achievable rate becomes [31]

The outage and the ergodic performance of the SR with $K=1$ are analyzed in [28] and the analytical results show that at high SNR region, the ergodic rate of the secondary transmission increases by about $3$ bit/s/Hz when the SNR increases $10$ dB. For large $K$, the ergodic rate of SR under multi-antenna PTx and PRx are analyzed in [55] and the upper bound of the secondary rate increases with the number of receive antennas $M_{r}$ and decreases with the transmission period $K$, scaling like $\frac{1}{K}\log_{2}(KM_{r})$ at high SNR region. The achievable rate region of SR for large $K$ under binary modulation at STx is analyzed in [26] and the results exhibit that the rate region of SRN is strictly larger than that of the conventional time division multiple access (TDMA) scheme.

For single-input-single-output (SISO) primary channel, the PTx is able to control its transmit power and the STx is able to adjust its reflection efficiency to fulfill specific performance metric requirements. The power and reflection efficiency constraints can be described as follows:

• •

  Peak Transmit Power Constraint: Let $P_{pk}$ be the peak power available at the transmitter, the power constraint is given by

  $$p\leq P_{pk}.$$

  (21)

• •

  Average Transmit Power Constraint: When the long-term power budget $P_{av}$ is considered, we have

  $$\mathbb{E}[p]\leq P_{av}.$$

  (22)

  The expectation is usually taken over the channel realizations.

• •

  Reflection Efficiency Constraint: The reflection efficiency $\alpha$ can be adjusted in each fading block. It needs to satisfy the constraint:

  $$0\leq\alpha\leq 1.$$

  (23)

With the aforementioned constraints, the weighted sum ergodic rate maximization problem under the peak/average power constraint is considered in [30]. Specifically, by jointly optimizing the transmit power $p$ at the PTx and the reflection efficiency $\alpha$ at the STx, the ergodic weighted sum rate is maximized under either long-term or short-term transmit-power constraint over the fading states. It is shown that the larger the $\alpha$ is, the better performance for both systems is obtained, due to the mutualism of the two systems. Compared to the ergodic rate optimization in the CR networks [53] where the secondary system has to keep its interference level to the primary receiver below a pre-designed threshold, the STx in the SR does not need to consider the interference constraint. The power allocation problem under the average power constraint for three SR paradigms is considered in [27] and the outage probabilities for these paradigms based on the optimized $\alpha$ and $p$ are analyzed in [56]. It is shown that under different transmission schemes, there are different relationships between the achievable rates of the primary and secondary transmissions.

For multiple-input-single-output (MISO) primary channel, spatial degrees of freedom can be exploited to balance the objectives of the primary and secondary systems. Here are some constraints to be considered:

• •

  Transmit Beamforming Constraint: For a given power budget $P_{t}$, the transmit beamforming vector ${\mathbf{v}}$ should satisfy the following constraint

  $$\left\|\mathbf{v}\right\|^{2}\leq P_{t}.$$

  (24)

• •

  Primary Transmission Rate Constraint: In the scenario where the primary system is sensitive to its transmit rate, the rate constraint should be considered, which is described as

  $$R_{s}\geq\tilde{R}_{s},$$

  (25)

  where $\tilde{R}_{s}$ is the minimum primary transmission rate requirement.

• •

  Secondary Transmission Rate Constraint: The secondary transmission rate constraint is given by

  $$R_{c}\geq\tilde{R}_{c},$$

  (26)

  where $\tilde{R}_{c}$ is the minimum secondary transmission rate requirement.

Considering the above constraints, by optimizing the transmit beamforming vector and the transmit power at the PTx, the weighted sum rate maximization problem and the transmit power minimization problem have been studied in [31]. By solving these optimization problems, the proposed SR not only enables the opportunistic transmission for the secondary system, but also enhances the primary transmission by properly exploiting the additional signal path provided by the secondary transmission. Moreover, the achievable rate of STx in finite block-length regime is considered in [57] and both of transmit power minimization and energy efficiency maximization optimization problems under the achievable throughput constraints are formulated to design the beamforming vector at the PTx.

The STx can be designed with full-duplex function [58, 29], where the STx is able to absorb a fraction of the incident signal to decode the messages from PTx, and simultaneously transmit its own information to the SRx by backscattering the remaining part of the incident signal. In [29], a full-duplex STx is considered in the SR and the achievable rates of secondary system with Gaussian and QAM codewords are derived, based on which, the power minimization problem is formulated to design the reflection efficiency at the STx as well as the beamforming vector at the PTx. The results show that the SR with full-duplex STx outperforms the optimally designed half-duplex scheme.

Human health monitoring is one of the most promising applications for 6G. By placing a number of health monitoring sensors, such as pulse and temperature monitors, implantable devices on the body, instant health status can be transmitted to a central unit [77]. The size and power consumption for health monitoring devices are two major concerns for system design[8]. Traditional wireless sensor communication technologies, such as Bluetooth and Zigbee, are not suitable for this application since the active communication scheme will cause high power consumption and the heat generated during communications may be harmful to the body, especially for the implantable sensors.

SR is a promising technology to overcome the above challenges due to the following reasons. First, the information transmission uses a passive way and thus the power consumption is ultra-low. Second, the basic backscattering radio circuits is very simple, so the size of the health monitoring devices can be very tiny. Last but not least, the health monitoring sensors can use the existing cellular infrastructure to transmit messages, which is beneficial and economical for service providers. For example, the user’s mobile phone can act as the center for health information collection by using the uplink/downlink cellular signals.

Smart home is another promising application in future wireless networks, in which almost all ubiquitous electronic devices within a house are connected to build an automatic and intelligent home environment [78]. In such a scenario, communications among different electronic devices generally feature low data rate and low power consumption, which places SR as the promising solution for smart home by backscattering the WiFi signals in the house. Specifically, a WiFi access point (AP) installed in the house or the user’s mobile phone can be the information center for monitoring the home environment status, such as air quality, room temperature, and usage of electronic devices.

Environmental monitoring represents the processes for continuously monitoring the environment status, such as atmospheric and weather conditions, through deploying wireless sensors [79]. The power consumption for the wireless sensors and the transmission range are two major concerns for the system design. SR can be considered as an environmental monitoring network due to its low cost and low power-consumption. Specifically, all the monitoring devices sense the environmental conditions and then feedback them to the receivers by modulating their messages over the strong broadcast TV signals. Compared with the other wireless techniques, such as LoRa and NB-IoT, the SR technology uses the existing cellular network infrastructure with lower power consumption and low cost, and thus is easier to be widely deployed.

It is important to establish accurate channel models to capture the essential behavior of the backscattering channels. In particular, due to the multiplicative nature of the backscattering channels, there are additional channel properties to be considered, such as rank-deficient, channel correlation and so on. There are some channel estimation schemes to estimate the cascade backscattering channels, e.g.,[80, 81, 82, 83]. However, most of them are with high complexity. It is highly desired to design low complexity algorithm or conduct joint channel estimation and signal detection. Besides, considering the massive devices, pilot symbols need to be carefully designed to support effective channel estimation, yet to avoid pilot contamination.

The synchronization between the primary and secondary symbols at the STx is a considerable problem. The carrier phase and timing recovery circuitry in the traditional synchronization algorithm requires the oscillator component, which is power-consuming [84]. Thus, it is very important to design a low complexity signal synchronization algorithm for the STx to track the primary signals on the RF level . Another considerable problem at the receiver is how to capture the weak backscattering link signal from the strong direct link signal. The use of active load or multiple antennas at the STx is one solution to enhance the strength of the backscattering link signal. However, considering the fact that both active load and the use of multiple antennas consume power, it remains an open problem on how to design energy-efficient active load and multiple antenna solutions.

Security and privacy are always critical in wireless communication systems. Due to the symbiotic relationship between the primary and secondary transmissions in SR, if one attacker disrupts the primary transmissions, the secondary transmission may be affected. It is an urgent problem to design a suitable and effective policy to ensure the security and privacy of the SR.