Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader

by   Deepak Mishra, et al.
Linköping University

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and combiner designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.



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I Introduction and Background

Backscatter communication (BSC) technology, comprising low-cost tags, without any bulkier radio frequency (RF) chain components, has gained significant recent attention owing to its potential in realizing the sustainable and pervasive ultra-low-power networking [2]. The key merit of BSC, not requiring any signal modulation, amplification, or retransmission by the tags, is that it shifts the high cost and large form-factor constraints to the reader side, leading to the tag-size miniaturization, which is the basic need of numerous smart networking applications [3]. Despite these potential merits, the widespread utility of BSC is limited by shorter read-range [4] and lower achievable data rates [5]

. Further, since the tags are lightweight, passive, chipless, and battery-free devices that do not have their own radio circuitry to process incoming signals or estimate the channel response, multiple antennas at the reader are required to separate out the backscattered signals from multiple tags by exploiting spatial multiplexing to enhance data rate and BSC reliability. Also, this multiantenna reader can implement energy beamforming (EB) during the carrier transmission to significantly improve BSC range. Therefore, to enable efficient BSC from multiple tags, there is a need for investigating the novel jointly-optimal transmit (TX) and receive (RX) beamforming at the multiantenna reader and backscattering designs at the tags.

I-a State-of-the-Art

BSC is based on the decoding of backscattered information signals at the reader as received from the multiple low-power tags. These tags communicate their information to the reader by respectively modulating their load impedances to control the strength, phase, frequency, or any other characteristics of the carrier signal(s) as received and reflected back to the reader. Depending on the energy constraints of the tags, BSC models can be divided into three groups: (a) passive [6], (b) semi-passive [7], and (c) active [8]. Though both passive and semi-passive tags depend on the carrier signal excitation from the reader, the latter are also equipped with an internal power source to enable better reliability and longer range of accessibility. Whereas, the active tags are battery-powered and can broadcast their own signal, thereby achieving much longer high link quality read range at the cost of bulkier size and higher maintenance requirements. Similarly, based on the network configuration, three main types of BSC models are:

  • Monostatic: With carrier emitter and backscattered signal reader being the same entity, this model can share antennas for transmission to and reception from tags [9].

  • Bi-static: Here, the emitter and reader are geographically-separated two different entities [4]. This model can help in achieving a longer range.

  • Ambient: Widely investigated model where emitter is an uncontrollable source and the dedicated reader decodes the resulting backscattered information from the tags [8].

As a consequence, monostatic configurations are cheaper because they require relatively smaller number of antenna elements due to their sharing in full-duplex settings. In contrast, the bi-static architectures ones can achieve longer read-range at the expense of combined higher antenna count for emission and reading purposes due to the geographic-separation of emitter and reader. As shown in Fig. 1, we investigate a monostatic BSC system with multiple single-antenna semi-passive tags and a multiantenna reader working in full-duplex mode [9, 10]. Henceforth, each antenna element at the reader is used for both carrier emission and backscattered signal reception [11]. This adopted configuration with a large antenna array at reader can maximize the BSC range, while meeting the desired rate requirements of tags, by exploiting the array gains during the downlink carrier transmission to the multiple tags, and uplink multiplexing gains during backscattered signals reception at the reader. Also, this setting is one of the most practical ones because it moves the computational-complexity and form-factor constraints from the low-power tags to a relatively-powerful reader. Recent, experimental results [12, 13] have corroborated this fact that coverage range can be significantly improved up to a few hundred meters by exploiting array gains at reader. However, these gains in the multiple-input-multiple-output (MIMO) reader-assisted BSC can be strongly enhanced by optimally designing the underlying transceiver (TRX).

Fig. 1: Monostatic reciprocal-BSC channel model with full-duplex multiantenna and multiple single-antenna tags.

Noting that the tags-to-reader backscatter uplink channel is coupled to the reader-to-tags downlink one, novel higher order modulation schemes have been investigated in [9] for the monostatic MIMO-BSC settings. Whereas, a frequency-modulated continuous-wave BSC system with monostatic reader, whose one antenna was dedicated for transmission and remaining for the reception of backscattered signals, was studied in [14] to precisely determine the number and position of active tags. On similar lines, considering a multiantenna power beacon assisted bi-static BSC model, robust inference algorithms, not requiring any channel state or statistics information, were proposed in [15]

to detect the sensing values of multiple single antenna backscatter sensors at a multiantenna reader by constructing Bayesian networks and using expectation maximization principle. Pairwise error probability and diversity order achieved by the orthogonal space time block codes over the dyadic backscatter channel (i.e., monostatic BSC system with multiple-antennas at the reader for transmission and reception from a multiantenna tag) were derived in

[9] and [16]. Authors in [17] designed a data detection algorithm for an ambient BSC system where differential encoding was adopted at the tag to eliminate the necessity of channel estimation (CE) in minimizing the underlying sum bit error rate (BER) performance. The asymptotic outage performance of an adaptive ambient BSC scheme with Maximum Ratio Combining (MRC) at the multiantenna reader was analyzed in [18] to demonstrate its superiority over the traditional non-adaptive scheme. Adopting the BSC model with multiple antennas the reader, authors in [19] first presented maximum likelihood (ML) based optimal combiner for simultaneously recovering the signals from emitter and tag. Then, they also investigated the relative performance of the suboptimal linear combiners (MRC, Zero Forcing (ZF) and Minimum Mean-Squared Error (MMSE)) and successive interference cancellation (SIC) based combiners, where MMSE-SIC combiner was shown to achieve the near-ML detection performance. In [20], a dyadic backscatter channel between multiantenna tag and reader was studied to quantify the impact of underlying pin-hole diversity and the RF tag’s scattering aperture on enhancing the achievable BER performance and tag operating range. Authors in [21] noted that if separate reader transmitter and receiver antennas are used in conjunction with multiple RF tag antennas, the envelope correlation between the forward and backscatter links can be significantly reduced to enhance the BER performance. Furthermore, investigating the optimal detection threshold for ambient BSC in [22], it was found that an increasing array size can yield larger gains in BER at low signal-to-noise (SNR), with lower returns in high SNR regime.

On different lines, with the goal of optimizing harvested energy among tags, sub-optimal EB designs for monostatic multiantenna reader were investigated in [23]. More recently, a least-squares-estimator for the BSC channels between a multiantenna reader and single-antenna tag was proposed in [10]. Based on that a linear MMSE based channel estimator was designed in [24] to come up with an optimal energy allocation scheme maximizing underlying single-tag BSC performance while optimally selecting number of orthogonal pilots for CE.

I-B Notations Used

The vectors and matrices are respectively denoted by boldface lower-case and capital letters.

, , and respectively denote the Hermitian transpose, transpose, and conjugate of matrix . and respectively represent zero, all-ones, and identity matrices. stands for -th element of matrix , represents for -th column of , and stands for -th element of vector . With and respectively being the trace and rank of matrix , and respectively represent Frobenius norm of a complex matrix and absolute value of a complex scalar. is used to denote a square diagonal matrix with ’s elements in its main diagonal and for representing the vectorization of matrix into a column vector. and represent the inverse and square-root, respectively, of a square matrix , whereas means that is positive semidefinite and operator represents the Hadamard product of two matrices. Expectation is defined using and

represents the principal eigenvector corresponding to maximum eigenvalue

of a Hermitian matrix . Lastly, with , and respectively denoting the real and complex number sets,

denotes the complex Gaussian distribution with mean

and covariance .

Ii Motivation and Significance

This section first discusses the novel aspects of this work targeted towards addressing an important existing research gap along with the potential scope of the proposed designs. In the latter half, we summarize the main contributions of this paper.

Ii-a Novelty and Scope

Since the information sources in BSC, i.e., tags, do not have their own RF chains for communication, the two key roles of the reader are: (a) carrier transmission to excite the tags in the downlink, and (b) efficient detection of the received backscattered signals in the uplink. Therefore, new TRX designs are needed because the requirements of RX design for the uplink involving effective detection of the backscattered information signals at the reader as received from the multiple tags are different from those of the TX beamforming in the downlink involving single-group multicasting-based carrier transmission. Furthermore, the underlying nonconvex optimization problem is more challenging than in conventional wireless networks because the corresponding backscattered throughput definition involves product or cascaded channels. Also, the resource-limitations of tags put additional constraints on the precoder and combiner designs.

The existing works [11, 14, 12, 13, 15, 9, 16, 19, 23, 10, 24, 22, 25] on multiantenna reader supported BSC did not focus on utilizing reader’s efficacy in designing smart signal processing techniques to overcome the radio limitations of tags by jointly exploiting the array and multiplexing gains. To the best of our knowledge, the joint TRX design for the multiantenna reader has not been investigated in the literature yet. Also, the backscattering coefficient (BC) optimization at the tags for maximizing the sum-backscattered-throughput is missing in the existing state-of-the art on the multi-tag BSC systems. Recently, a few BC design policies were investigated in [25] for maximizing the average harvested power due to the retro-directive beamforming at multiantenna energy transmitter based on the backscattered signals from the multiple single antenna tags. But [25] ignored the possibility of uplink backscattered information transfer, and only focused on the downlink energy transfer.

In this work, we have presented novel design insights for both TRX and BC optimization. Specifically, new solutions for the individually-optimal designs and asymptotically-global-optimal joint-designs are proposed along with an efficient low-complexity iterative algorithmic implementation. These designs can meet the basic requirement of extending the BSC range and coverage by imposing the non-trivial smart signal processing at multiantenna reader. Significance of the proposed designs is corroborated by the fact that they can yield substantial gains without relying on any assistance from the resource-constrained tags in solving the underlying nonconvex sum-backscattered-throughput maximization problem. Our optimal designs are targeted for serving applications with the overall BSC system-centric goal, rather than individual tag-level, where the best-effort delivery is desired to maximize the aggregate throughput. Practical utility of these designs targeted for monostatic BSC can be easily extended for addressing the needs of other BSC models. Also, we discourse how the proposed optimization techniques can be used for solving the nonconvex throughput maximization problems in wireless powered communication networks (WPCN). Thus, this investigation, providing designs for achieving longer read-range and higher backscattered-throughput, enables widespread applicability of BSC technology in ultra-low-power emerging-radio networks for last-mile connectivity and Internet-of-Things networking.

Ii-B Key Contributions and Paper Organization

Five-fold contribution of this work is summarized below.

  • A novel optimization framework has been investigated for maximizing the sum-backscattered-throughput from multiple single-antenna tags in a monostatic BSC setting. It involves: (i) smart allocation of reader’s resources by optimally designing the TRX, and (ii) maximizing the benefit of tags cooperation by optimally designing their BC. The corresponding basic building blocks and problem definition addressed are presented in Section III.

  • Noting non-convexity of this joint problem, we propose new individually-optimal designs for TX precoding, RX beamforming at reader, and BC at the tags in Section IV. Further, their respective generalized-convexity [26] is explored along with individual global-optimality.

  • Next, the asymptotically-optimal joint designs are derived in Sections V-A and V-B for the high and low SNR regimes, respectively. We show that both these jointly-optimal designs, which can be efficiently obtained, provide key novel design insights. Using these results as performance bounds, a low-complexity iterative-algorithm is outlined in Section V-C to obtain a near-optimal design.

  • To corroborate the practical utility of the proposed designs, in Section VI we discuss their extension to address the requirements of application networks like WPCN, bi-static and ambient BSC models with imperfect channel state information (CSI) and multiantenna tags.

  • Detailed numerical investigation is carried out in Section VII to validate the analytical claims, present key optimal design insights, and quantify the performance gains over the conventional designs. There other than comparing the efficacy of individually-optimal designs, we have shown that on an average sum-throughput gains can be achieved by the proposed joint TRX and BC design over the relevant benchmarks [27, 19].

Throughout this paper, the main outcomes have been highlighted as remarks and Section VIII concludes this work with the keynotes and possible future research extensions.

Iii Problem Definition

We start with briefly describing the adopted system model and network architecture, followed up by the BSC and semi-passive tag models. Later, we present the expression for the achievable backscattered-throughput at reader from each tag.

Iii-a System Model and Network Architecture

We consider a multi-tag monostatic BSC system comprising single-antenna semi-passive tags, and one full-duplex reader equipped with antennas which is responsible for simultaneous carrier transmission and backscattered signal decoding. Hereinafter, the -th tag is denoted by with and the reader is denoted by . We assume that these tags are randomly deployed in a square field of length meters (m), with being at its center as shown in Fig. 1. To enable full-duplex operation [11], each of the antennas at can transmit a carrier signal to the tags while concurrently receiving the backscattered signals.

The multiantenna adopts linear precoding and assigns each a dedicated precoding vector . We denote by the vector of independent and identically distributed (i.i.d.) symbols as simultaneously transmitted by . Hence, the complex baseband transmitted signal from is given by , and we assume that there exists a total power budget to support this transmission. The resulting modulated reflected data symbols as simultaneously backscattered from the tags are respectively spatially separated by with the aid of linear decoding vectors as denoted by . Here combiner is used for decoding ’s message. This restriction on TRX designs to be linear has not only been considered to address low-power-constraints of BSC, but also because for , these designs are nearly-optimal [28].

Iii-B Adopted BSC and Tag Models

In contrast to the practical challenges in implementing the full-duplex operation in conventional communication systems involving modulated information signals, the unmodulated carrier leakage in monostatic full-duplex BSC systems can be efficiently suppressed [11]. Further, we consider semi-passive tags [7] that utilize the RF signals from for backscattering their information and are also equipped with an internal power source or battery to support their low power on-board operations. Thus, they do not have to wait for having enough harvested energy, thereby reducing their overall access delay [8]. However, note that this battery is only used for powering the tag’s circuitry to set the desired modulation or BC and for regular operations like sensing. Also, these benefits of longer BSC range and higher rate due to an on-tag battery suffer from few problems like extra weight, larger size, higher cost, and battery-life constraints.

For implementing the backscattering operation, we consider that each modulates the carrier received from via a complex baseband signal denoted by  [4]. Here, the load-independent constant is related to the antenna structure of the th tag and the load-controlled reflection coefficient switches between the distinct values to implement the desired tag modulation [29]. Without the loss of generality, to produce impedance values realizable with passive components, we assume that the effective signal from each tag satisfies because the scaling factor corresponding to the magnitude of the ’s complex baseband signal can be included in its reflection coefficient or BC definition [6]. The higher values of reflect increasing amounts of the incident RF power back to which thus result in higher backscattered signal strength and thereby maximizing the overall read-range of . Whereas, the lower value of BC for a tag implies that its backscattering to causes lesser interference for the other tags.

The -to- wireless reciprocal-channel is denoted by an vector . Here, parameter represents average channel power gain incorporating the fading gain and propagation loss over -to- or -to- link. Although we have considered i.i.d. fading coefficients for all -to- channels due to sufficient antenna separation at reader [16, 9, 24], the proposed designs in this work can also be used for the BSC settings with dependent and not necessarily identically distributed fading scenarios. In this paper we assume that this perfect CSI for each is available at to investigate the best achievable performance. However, our proposed designs can be extended to imperfect CSI cases as discussed in Section VI-A2 and their robustness under inaccuracy in CSI is also demonstrated in Section VII.

Therefore, on using these models, the baseband received signal at is expressed as:


where for each are i.i.d. symbols and

is the zero-mean Additive White Gaussian Noise (AWGN) vector with independent entries having variance


Iii-C Backscattered-Throughput at

We note that the backscattered noise strength due to the AWGN power is practically negligible [6, 7, 8, 9, 4, 10] in comparison to the corresponding carrier reflection strength due to the signal power . So, ignoring this backscattered noise, which in comparison to the excitation power gets practically lost during backscattering from tags, the received signal available for information decoding at , as obtained using the definition (1), is:


where the vector represents the received zero-mean AWGN at and is the noise power spectral density. Applying the linear detection at , the received signal can be separated into streams by multiplying it with detection matrix and the corresponding decoded information signal is:


As each of the streams can be decoded independently, the complexity of the above linear receiver is on the order of , where denotes the cardinality of the finite alphabet set of for each . Thus, with , the th element of , to be used for decoding the backscattered message of , is:


Therefore, using (III-C) and the backscattered message definition from Section III-B, the resulting signal-to-interference-plus-noise-ratio (SINR) at from each is given by:


where denotes the effective transmit SNR at as realized due to the carrier transmission from , which itself on ignoring the backscattered noise can be defined as:


Thus, the backscattered-throughput for at is given by:


From the above throughput definition which has been extensively used in existing multi-tag BSC investigations, we notice that the key difference from the throughput in conventional networks is the existence of the product or cascaded channels definition and additional BC parameters.

Lastly, the resulting sum-backscattered-throughput , which is the system-level performance metric as maximized in the current multi-tag monostatic BSC setting, is given by:


Next we use the above sum-throughput definition for carrying out the desired optimization of TRX and BC designs.

Iv Sum Backscattered Throughout Maximization

Here we first mathematically formulate the joint optimization problem in Section IV-A and discuss its salient features. Next, after discussing the reasons for non-convexity of the problem, we present the individual optimization schemes for obtaining the optimal TX precoding, RX beamforming, and BC designs in Sections IV-BIV-C, and IV-D, respectively.

Iv-a Mathematical Optimization Formulation

The joint reader’s TRX and tags’ BC design to maximize the achievable sum-backscattered-throughput at , as defined in (8), can be mathematically formulated as below:

where is the available transmit power budget at , and respectively the practically-realizable [6] lower and upper bounds on BC for each tag . All the computations for obtaining the jointly-optimal solution of are performed at , which then sets its TRX to the optimal one and instructs the tags to set their respective BC accordingly. Here, the battery energy consumption at semi-passive tags in setting their respective BC as per ’s instruction is negligible in comparison to their regular operations [7].

We notice that although has convex constraints, it is in general a nonconvex optimization problem because its nonconcave objective includes coupled terms involving the product of optimization variables, i.e., precoders , combiners , and BC for each . Despite the non-convexity of joint optimization problem , we here reveal some novel features of the underlying individual optimizations that can yield the global-optimal solution for one of them while keeping the other two fixed. In other words, we decouple this problem into individual optimizations and then try to solve them separately by exploiting the reduced dimensionality of the underlying problem. We next discuss the individual optimizations, one-by-one, starting with the TX precoder optimization at during the downlink carrier transmission.

Iv-B Optimal Transmit Precoding Design at

The proposed method for obtaining the optimal TX beamforming vectors for each at can be divided into two parts. In the first part, we discourse the relationship between precoder designs for the different tags in the form of Lemma 1. Thereafter proving the concavity of the equivalent semidefinite relaxation (SDR) [30] for the precoder design optimization problem, the randomization process [31] is used to ensure desired implicit rank-one constraint on the matrix solution. The implementation steps are provided in the form of Algorithm 1.

Lemma 1

The optimal precoder designs for the tags that maximize the resulting sum-backscattered-throughput are identical. In other words, for each .


The proof is given in Appendix A.

Lemma 1 actually implies that transmits with same precoder for all the tags, i.e., multicasting is optimal TX design. This is due to the fact that carrier transmission from is just to effectively excite (power-up) the tags, and this excitation can be made most efficient when the TX precoder aligns with the strongest eigenmode of the matrix . A similar observation was made in context of the precoder designs for efficient downlink energy transfer in WPCN [33, 34].

Subsequently, with the above result, the sum-backscattered-throughput can be rewritten below as a function of the common precoding vector , satisfying , RX beamforming matrix and BC vector for tags:


where backscattered-throughput as function of , received SINR as a function of , and transmit SNR as a function of for are respectively defined as:


However, since is still non-concave in , we next show that by using an equivalent SDR with matrix definition satisfying rank-one constraint, we can resolve this issue.

Lemma 2

The sum-backscattered-throughput, for a given combiner design for and BC vector for the tags, is a concave function of the matrix variable .


Please refer to Appendix B for details.

1: Channel vectors , combiners , BC , random samples , budget , and tolerance .
2:Global optimal transmit precoding vector
3:Set and .
4:do Iteration
5:     Set .
6:     Set .
7:      Solve the convex problem below, satisfying DCP using CVX, and set its global-optimal solution to
8:      Set
9:while  Termination
10:Set , apply eigenvalue decomposition to obtain: , and set . Randomization
11:Generate independent unit-variance zero-mean circularly-symmetric complex Gaussian vectors and

independent uniformly distributed random vectors

on , where both these vectors are in size.
12:Set where and
14:     Set .
15:     Set .
16:     Set .
17:     Update
19:Set .
20:if  then
21:     Set .
22:else if  then
23:     Set .
25:     Set .
Algorithm 1 Iterative algorithm for precoder optimization.

Following Lemmas 1 and 2 along with definition (B.1), the TX beamforming optimization for a given combiner and BC design can be formulated as the optimization problem below:

which on ignoring is a convex problem with objective function to be maximized being concave in (cf. Lemma 2) and constraints being convex. So, although the optimal solution of can be obtained using any standard convex optimization toolbox, like the CVX MATLAB package [36], there lie two challenges. First, the objective function does not satisfy the Disciplined Convex Programming (DCP) rule set for using the CVX toolbox [36, 32] because each summation includes the ratio of linear functions of . Second issue is that the optimal as obtained after solving the SDR needs to implicitly satisfy the rank-one constraint .

The first of the above-mentioned issues can be resolved using the recently proposed quadratic transform technique for maximizing the multiple ratio concave-convex linear fractional programming problems [37]. Further as on ignoring , is a convex problem, the stationary point as obtained using this quadratic transformation yields the global-optimal solution of . Hence, we can obtain the global-optimal by solving SDR using CVX toolbox. Thereafter, the second issue can be resolved by deploying the randomization process [31] to ensure the implicit satisfaction of the rank-one constraint . The detailed algorithmic steps resolving these two issues are summarized in iterative Algorithm 1. It starts with an initial precoding matrix with , which is motivated by the fact that for single-tag case, the respective maximal ratio transmission (MRT) design is optimal [24]. Then, after initializing the auxiliary variable vector as in step 5, we apply the quadratic transformation as suggested in [37, Theorem 1] to each underlying SINR term in (B.1) and maximize the corresponding convex reformulation with respect to , for a given , as denoted by in step 7. Thereafter, we continue to update and optimize in an iterative fashion. Since the sum-backscattered-throughput is concave in , this sequence of convex optimization problems converges to a stationary point of , which is also its global-optimal solution, with nondecreasing values for the underlying objective after each iteration. When this improvement in the throughput value reduces below a certain acceptable threshold, the Algorithm 1 terminates with the global-optimal precoding matrix . Next, for this precoding solution to satisfy the rank-one constraint we deploy the randomization process [30] as given by steps 10 to 25 of Algorithm 1 which returns the optimal TX precoder . The randomization process involves generation of set of candidate weight vectors and selecting the one which yields the highest sum-backscattered-throughput among them. Here, we have set samples as mentioned in the results section of [31] because it maintains a good tradeoff between the solution quality and complexity.

Iv-C Receive Beamforming or Combiner Design at Reader

For a given precoder and BC , the optimal RX beamforming problem is formulated as:

Below we outline a key result defining the optimal RX beamforming or combiner design at .

Lemma 3

For a given precoder design for and BC vector for the tags, the optimal combiner design is characterized by the Wiener or MMSE filter, as defined below:


Firstly, from (5) and (7) we notice that for each depends only on its own combiner . Accordingly, we can maximize the individual rates or SINRs in parallel with respect to , while satisfying their underlying normalization constraint . Further, as the in (5) can be alternatively represented as a generalized Rayleigh quotient form [38, eq. (16)], the optimal combiner for each , can be obtained as the generalized eigenvector of the matrix set with largest eigenvalue. Using it along with and Lemma 1, the optimal combiner in (11) is obtained.

Iv-D Backscattering Coefficient (BC) Optimization at Tags

Mathematical formulation for this case is presented below:

We would like to mention that although is a nonconvex problem, it can be solved globally, but in non-polynomial time, using the block approximation approach [39]. Furthermore, even though the sum-backscattered-throughput defined in (8) is nonconcave function of the BC vector , below we present a key property for the backscattered-throughput for each , which we have exploited in designing a computationally-efficient solution methodology.

Proposition 1

For a given precoder and combiner design for , both the backscattered SINR and throughput for each tag is pseudolinear in .


As SINR involves the ratio of two linear functions, and of , using the results of [40, Tables 5.5 and 5.6] we note that is both pseudoconvex and pseudoconcave in the BC vector . Now as functions which are both pseudoconvex and pseudoconcave are called pseudolinear [26], each is pseudolinear in . Further, since the monotonic transformations preserve pseudolinearity of a function [40], we observe that throughput is also pseudolinear in .

Here, it is worth noting that since the summation operation does not perverse pseudolinearity [26], the sum-backscattered-throughput maximization with respect to is not a convex problem and hence does not possess global-optimality. However, we notice that can be alternatively casted as an optimal power control problem for the sum-rate maximization over the multiple interfering links [41, and references therein]. For instance, recently in [37] an application of fractional-programming was proposed for efficiently obtaining a stationary point for the nonconvex power control problem over the multiple interfering links. We have used that to yield an efficient low-complexity suboptimal design for BC vector .

1: Channel vectors , precoder , combiners , and acceptable tolerance .
2:Suboptimal BC .
3:Set and .
4:do Iteration
5:     Update
6:     Set .
7:      Solve convex problem below and set the resulting global-optimal solution to
8:      Set
9:while . Termination
10:Return .
Algorithm 2 Suboptimal BC optimization algorithm.

The detailed algorithmic implementation is outlined in Algorithm 2. It starts with an initial BC vector with all its entries being which is motivated by the fact that for high-SNR regime, the optimal BC is characterized by the full-reflection mode. Then, after initializing the auxiliary variable vector as in step 5, we apply the quadratic transformation as suggested in [37, Theorem 1] to the underlying each SINR term and maximize corresponding convex reformulation with respect to , for a given , as denoted by in step 7. Thereafter, we continue to update and optimize in an iterative fashion. Since each throughput term is nondecreasing and concave in its respective SINR term, which itself is pseudolinear in , this sequence of convex problems converges to a stationary point of with nondecreasing values for the underlying objective after each iteration. When this improvement in throughput value reduces below a tolerance , the Algorithm 2 terminates with a near-optimal BC .

V Proposed Low-Complexity Optimal Designs

Using the key insights developed for the individually-optimal TX precoding, RX beamforming, and BC vector designs in previous section, now we focus on deriving the jointly-optimal TRX and BC designs by simultaneously solving the original joint optimization problem (but with for each based on Lemma 1) in the three optimization variables and . We start with presenting novel asymptotically-optimal joint designs for the TRX at and BC at tags in both low and high SNR regimes. In this context, first a joint design for low-SNR application scenarios is proposed, followed by the other one for the high-SNR regime. These two efficient low-complexity asymptotically-optimal designs shed new key design insights on the bounds for jointly-global-optimal solution. Thereafter, we conclude by presenting a Nelder–Mead (NM) method [26, Ex. 8.51] based low-complexity iterative algorithm that does not require the explicit computation of complex derivatives for the objective sum-backscattered-throughput.

V-a Asymptotically-Optimal Design Under High-SNR Regime

First from Lemma 3 we revisit that regardless of the precoder and BC design, the optimal combiner is characterized by the MMSE filtering defined in (11). Next, we recall that under the high-SNR regime, the ZF-based RX beamforming is known to be a very good approximation for the Wiener or MMSE filter [38, eq. (14)]. So, using the definition below,


the ZF based combiner matrix is given by:


As the RX beamforming vector has to satisfy constraint , the optimal combiner for the high-SNR scenarios, as obtained from in (13), is given below:


Here, the ZF-based RX beamforming vectors satisfy:


Thus, with , the sum-backscattered-throughput under high-SNR regime where employs ZF based combiner , is given by:


Next revisiting the matrix definition , the equivalent SDR for jointly optimizing the remaining variables and is formulated below as , which is followed by Lemma 4 outlining a key result to be used for solving it.

Lemma 4

is concave in with optimal being equal to for each .


Please refer to Appendix C for the proof.

Using Lemma 4 and ignoring , we notice that , with for each tag, is a convex problem in the optimization variable . Further, since satisfies the DCP rule, the CVX toolbox can be used to obtain the optimal , as denoted by . However, for this precoding solution to satisfy the rank-one constraint we need to deploy the randomization process, as discussed in Section IV-B and implemented via steps 10 to 25 of Algorithm 1 while setting in step 10, to finally get the optimal precoder .

Remark 1

Under high-SNR regime, optimal precoder is obtained by solving SDR with followed by randomization process. Whereas, optimal combiner follows ZF based design and all the tags are in full-reflection mode, i.e., BC vector .

V-B Novel Joint Design For Low-SNR Applications

Under low-SNR regime, we can use the following two approximations for simplifying :


where (17a) is owing to the fact that under low-SNR regime, the backscattered signals from all the other tags, causing interference to the tag of interest, is relatively very low in comparison to the received AWGN. Whereas, (17) is obtained using the approximation . Using these properties, the sum-backscattered-throughput reduces to:


where is based on the individual optimizations of combiner and BC vector respectively following MRC and full-reflection mode in this scenario. So, with above as objective and as variable, the corresponding maximization problem can be formulated as below:

From , we notice that the TX precoder design at that maximizes the sum received power at the tags also eventually yields the maximum sum-backscattered-throughput from them. Thus, the optimal precoder, same for all tags and called TX energy beamforming (EB), is denoted by:


where is the right singular vector of the matrix that corresponds to its maximum eigenvalue . So, the total sum received power at tags is:


On substituting and , for each , in (11) and using Lemma 3, the optimal combiner (MMSE based design) at tag for the low-SNR regime is given by: