A method for designing a limited dynamic range constraint transmit waveform of a target-oriented side photovoltaic receiver light channel integration system

Abstract

The application discloses a kind of target-oriented photovoltaic receiver light communication and energy integration system limited dynamic range constraint emission waveform design method, first generate MLS sequence, simultaneously to the original signal of input PAM modulation obtains PAM sequence, PAM sequence is as slow envelope to MLS sequence mean envelope modulation, and the alternating current signal after envelope modulation is obtained, direct current bias B is superimposed in the alternating current signal to constitute the final emission waveform.The application B determines reference light intensity and effective AC budget.In available budget, communication level interval and perception MLS amplitude are determined by amplitude allocation factor ρ together.Under fixed AC budget, ρ determines the trade-off between communication and perception, and B determines the upper limit of feasible dynamic range.The application makes full use of limited amplitude resources, realizes low-bandwidth photovoltaic receiver-oriented efficient information extraction, accurate echo perception and stable energy transmission, and realizes flexible coordination of communication and energy functions.

Description

Technical Field

[0001] This invention relates to an integrated optical communication and sensing system, specifically to a finite dynamic range constrained transmit waveform design method for an integrated optical communication and sensing system for a target-side photovoltaic receiver, belonging to the field of wireless optical communication technology. Background Technology

[0002] As the digital transformation of industrial systems deepens, the deployment of massive industrial IoT devices for equipment monitoring, process control, and data-driven industrial intelligence is accelerating. In these scenarios, future wireless infrastructure needs to provide reliable data links, accurate environmental sensing, and sustainable power support for a vast number of low-power terminals. This demand has driven research into integrated communication, sensing, and power transfer (O-ICSPT) technology, which aims to achieve these three functions within a unified signal and hardware framework. Compared to radio frequency (RF) technology, optical wireless technology offers advantages such as abundant spectrum resources, resistance to electromagnetic interference, strong spatial directionality, and high physical layer security. Therefore, in industrial environments characterized by dense equipment, harsh electromagnetic environments, and stringent safety requirements, O-ICSPT technology has demonstrated significant application potential.

[0003] Regarding the integrated optical communication, sensing, and energy harvesting system, the applicant previously filed three invention patents. Patent application number 202510172128.4, entitled "Integrated Optical Communication, Sensing, and Energy Harvesting System," first proposed and experimentally verified an integrated optical system that simultaneously supports communication, sensing, and energy transmission. Experimental results show that the transmitted waveform of the integrated optical communication, sensing, and energy harvesting system can be equivalently decomposed into an alternating current (AC) component for communication and sensing and a direct current (DC) component for energy transmission, which work collaboratively through superposition and coupling. Based on this waveform structure characteristic, patent application number 202511977650.6, entitled "An Integrated Optical Communication, Sensing, and Energy Harvesting System and Transmitted Waveform Design Method," further proposed a hybrid waveform design scheme combining AC-to-AC amplitude allocation and AC-DC amplitude allocation. This achieves a flexible trade-off between communication, sensing, and energy harvesting performance in the integrated optical communication, sensing, and energy harvesting system, and utilizes a beam splitter-free integrated design of the target-side photodiode (PD) and photovoltaic (PV) to achieve a lighter hardware architecture. The aforementioned existing integrated optical communication and sensing systems all employ a hybrid PD / PV receiver architecture on the target side. However, this design not only increases the hardware complexity of the receiver but also introduces additional static power consumption. To address these issues, the invention patent application No. 2026104629547, entitled "An Integrated Optical Communication and Sensing System Based on a Target-Side Photovoltaic Receiver and a Transmit Waveform Design Method," proposes an integrated optical communication and sensing system for a target-side photovoltaic receiver that employs a flexible maximum long sequence (MLS)-pulse amplitude modulation (PAM) peak envelope waveform. This system achieves a lighter, more energy-efficient, and universally applicable system design, breaking the cascading constraints of low-bandwidth photovoltaic sampling rates, while simultaneously achieving a flexible trade-off between communication, sensing, and energy.

[0004] However, for the integrated optical communication and sensing energy system based on the target-side photovoltaic receiver, the MLS-PAM peak envelope waveform design method proposed in the invention patent "An Integrated Optical Communication and Sensing Energy System Based on the Target-Side Photovoltaic Receiver and a Transmit Waveform Design Method" mainly focuses on the peak envelope construction and its communication-sensing-energy coordination mechanism. It has not fully considered the inherent coupling relationship between the waveform mean distribution, amplitude allocation space, and the demodulation characteristics of the target-side photovoltaic receiver under the constraint of limited dynamic range. In fact, the limited dynamic range not only directly restricts the feasible amplitude range of the transmitted waveform but also simultaneously affects the communication mean decision, the intensity of the sensing correlation peak, and the energy harvesting level, making it a significant non-ideal factor restricting system performance improvement and engineering implementation.

[0005] Based on this, for the integrated optical communication, sensing and energy harvesting system of the target-side photovoltaic receiver, there is an urgent need to propose a waveform design method that considers the constraints of limited dynamic range. By coordinating the construction and resource allocation of the symbol-level mean envelope and the fast sensing component, a flexible trade-off between communication, sensing and energy harvesting performance in the integrated optical communication, sensing and energy harvesting system of the target-side photovoltaic receiver can be achieved. Summary of the Invention

[0006] To address the challenge of coordinating and optimizing the communication, sensing, and energy harvesting performance of a target-side photovoltaic receiver integrated optical communication, sensing, and energy harvesting system under limited dynamic range constraints, this invention aims to propose a limited dynamic range constraint transmit waveform design method for such systems. This invention fully utilizes limited amplitude resources by constructing an MLS-PAM mean envelope waveform and its resource allocation mechanism, achieving efficient information extraction, accurate echo sensing, and stable energy transmission for low-bandwidth photovoltaic reception, while also enabling flexible coordination of the three functions of communication, sensing, and energy harvesting.

[0007] The technical solution of this invention is implemented as follows:

[0008] A finite dynamic range-constrained transmit waveform design method for a target-side photovoltaic receiver-optical-sensing-energy integrated system is proposed. The target-side photovoltaic receiver-optical-sensing-energy integrated system includes a sensing signal generator for generating an integrated sensing digital signal, a transmitter, a photovoltaic receiver, an information decoding module, an energy harvesting module, and a sensing processing module. The sensing signal generator and transmitter are mounted on the transceiver, while the photovoltaic receiver, information decoding module, and energy harvesting module are mounted on the target side. The sensing signal generator includes an MLS modulation unit, a PAM modulation unit, and an envelope modulation unit. The MLS modulation unit utilizes a linear feedback shift register based on primitive polynomials to generate an MLS sequence. The system comprises a PAM modulation unit for PAM modulation of the input raw signal to obtain a PAM sequence, an envelope modulation unit for using the PAM sequence as a slow envelope to perform envelope modulation on the MLS sequence to obtain an envelope-modulated AC signal, and a DC bias superimposed on the envelope-modulated AC signal to complete AC-DC coupling to obtain an analog electrical signal. The transmitter converts the analog electrical signal into an optical signal, which is simultaneously connected to the sensing and processing module and the photovoltaic receiver. The photovoltaic receiver outputs the received optical signal as an electrical signal, which is then separated into a DC signal and an AC signal by a power separation unit. The DC signal is connected to the energy harvesting module, and the AC signal is connected to the information decoding module.

[0009] The transmitted waveform is the optical signal output by the transmitter, which is formed according to the following steps.

[0010] 1) First, generate an MLS sequence. Let the bipolar MLS sequence of length L be denoted as m[l]∈{-1,+1}, where l∈{0,1,…,L-1}; this sequence repeats periodically in the time domain; simultaneously, PAM modulation is applied to the original signal to obtain a PAM sequence; each communication symbol spans N. rep Each MLS sequence period contains N PAM symbols. sps =N repL sampling points; for the k-th PAM symbol, k∈{0,1,…,K-1}, its corresponding set of sampling points is defined as ;

[0011] 2) Envelope Modulation: For a second-order PAM sequence, the symbol-dependent communication level is defined as... Where Δ represents half of the communication level interval; correspondingly, the sign-related term μ k Given by the following formula

[0012]

[0013] Where b k ∈{0,1} represents the emitted binary symbol;

[0014] According to μ k The AC waveform within the k-th symbol interval, along with the MLS components, is represented as follows:

[0015]

[0016] Where S represents the amplitude assigned to the MLS; then the overall AC waveform containing K symbols after envelope modulation is expressed as follows:

[0017]

[0018] in This represents the integer division operator;

[0019] 3) Finally, a DC bias B is superimposed on the AC waveform to obtain the analog electrical signal, which is the transmitter output waveform. .

[0020] Furthermore, the kth transmitted signal satisfy Waveform parameters satisfy as well as A max For peak amplitude constraints; equivalently, the feasible communication envelope span and sensing amplitude must satisfy... The DC bias B simultaneously determines both the reference light intensity and the effective AC budget C. eff C eff Defined as

[0021]

[0022] Within the available budget C eff Within this range, the communication level interval Δ and the MLS amplitude S are jointly determined by the amplitude allocation factor ρ∈(0,1), expressed as:

[0023]

[0024] Substitute mapping relationship Then the final transmitted waveform of the k-th symbol is

[0025]

[0026] Therefore, the overall transmission waveform containing all K symbols is

[0027]

[0028] in .

[0029] Furthermore, the present invention sets the amplitude allocation factor ρ as an adjustable parameter, within a fixed AC budget C. eff The following approach balances communication and sensing through the value of ρ; the feasible upper limit of the dynamic range is determined by the DC bias B; under the constraint of finite dynamic range, the system performance is jointly determined by the DC bias B and the amplitude allocation factor ρ, where the DC bias B establishes the macroscopic performance boundary, and increasing B changes the AC budget C. eff This simultaneously alters the three functions of synesthesia; the amplitude allocation factor ρ affects the AC budget C. eff The levels are redistributed to achieve communication-oriented level spacing Δ and perception-oriented MLS amplitude S fluctuation.

[0030] Furthermore, for a sufficiently long equiprobable bitstream, the data-dependent communication term tends to zero in expectation; therefore, the time-averaged signal level of x[n] is approximately expressed as:

[0031]

[0032] Accordingly, the average DC optical power of the transmitted waveform is defined as .

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] Compared to existing MLS-PAM waveform designs based on peak envelopes, the MLS-PAM mean envelope waveform and its resource allocation mechanism proposed in this invention can fully utilize limited amplitude resources while adapting to the mean demodulation characteristics of low-bandwidth photovoltaic receivers, achieving synergistic optimization of communication, sensing, and energy harvesting performance. Under limited dynamic range conditions, by flexibly allocating AC amplitude resources between the communication mean interval and the sensing MLS component, the reliability of communication decisions and the distinguishability of echo correlation peaks are improved, thereby simultaneously improving bit error rate performance and ranging accuracy. By introducing joint constraint modeling of DC bias and AC dynamic range, the system improves energy harvesting efficiency while ensuring non-negative optical signal transmission and enhances its adaptability to actual optical non-ideal factors. Attached Figure Description

[0035] Figure 1 - A schematic diagram of the integrated optical communication and sensing system framework using a photovoltaic receiver on the target side of this invention.

[0036] Figure 2 - The waveform construction and correlation characteristic curves of the MLS-PAM mean envelope design proposed in this invention; wherein, (a) AC waveforms under different ρ; (b) transmitted waveforms under different combinations of B and ρ; (c) cross-correlation characteristic curves of different waveforms; (d) cross-correlation characteristic curves of the proposed MLS-PAM waveform under different ρ.

[0037] Figure 3 - The communication and sensing performance of this invention varies with signal-to-noise ratio (SNR) under different waveform parameter settings; wherein, (a) BER varies with SNR under different ρ values ​​when B=0.4 is fixed; (b) RMSE varies with SNR under different ρ values ​​when B=0.4 is fixed; (c) BER varies with SNR under different B values ​​when ρ=0.4 is fixed; (d) RMSE varies with SNR under different B values ​​when ρ=0.4 is fixed.

[0038] Figure 4 - The communication, sensing and energy harvesting performance curves of this invention under different waveform parameter settings with SNR=0 dB; wherein, (a) BER as a function of ρ under different B values; (b) RMSE as a function of ρ under different B values; (c) HE as a function of ρ under different B values; (d) BER as a function of B under different ρ values; (e) RMSE as a function of B under different ρ values; (f) HE as a function of B under different ρ values. Detailed Implementation

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0040] The target-side photovoltaic receiver optical communication and energy sensing integrated system framework of this invention is as follows: Figure 1As shown, the transceiver includes a sensing signal generator, a digital-to-analog converter, a transmitter, a photovoltaic receiver, an information decoding module, an energy harvesting module, and a sensing processing module for generating integrated sensing digital signals. The sensing signal generator, digital-to-analog converter, and transmitter are mounted on the transceiver, while the photovoltaic receiver, information decoding module, and energy harvesting module are mounted on the target side. The sensing signal generator includes an MLS modulation unit, a PAM modulation unit, and an envelope modulation unit. The MLS modulation unit generates an MLS sequence using a linear feedback shift register based on primitive polynomials. The PAM modulation unit modulates the input raw signal using PAM modulation to obtain the P... An AM sequence is used, and an envelope modulation unit is used to perform mean envelope modulation on an MLS sequence using a PAM sequence as a slow envelope to obtain an envelope-modulated AC signal. A DC bias is superimposed on the envelope-modulated AC signal to complete AC-DC coupling. The AC-DC coupled signal is converted into an analog electrical signal by a digital-to-analog converter. The transmitter is used to convert the analog electrical signal into an optical signal, which is simultaneously connected to a sensing processing module and a photovoltaic receiver. The photovoltaic receiver outputs the received optical signal as an electrical signal. The electrical signal is separated into DC and AC signals by a power separation unit. The DC signal is connected to an energy harvesting module, and the AC signal is connected to an information decoding module.

[0041] In this system, the transceiver illuminates the target node using the proposed MLS-PAM mean envelope waveform. A single large-area photovoltaic device on the target side simultaneously receives the incident light signal to achieve information extraction and energy harvesting. Furthermore, an retroreflector is integrated on the target side to reflect the incident waveform back to the transceiver for sensing. This invention relates to four parts: transmitted waveform design, communication decoding, sensing processing, and energy harvesting. Waveform design is fundamental and represents the core improvement of this invention. The four parts are described below.

[0042] 1. MLS-PAM Mean Envelope Waveform Design Method

[0043] To overcome the cascaded sampling rate limitation of low-bandwidth target-side photovoltaic receivers, the waveform proposed in this invention employs a slow average envelope for communication, thereby allowing symbol detection through envelope statistics rather than fast tracking. Since the slow envelope lacks the sharp correlation characteristics required for accurate delay estimation, this invention embeds a fast MLS component. This integrated design ensures robust sensing performance while preserving a photovoltaic-compatible communication format. Furthermore, a DC bias is introduced to ensure the non-negativity of light emission and achieve energy transfer. Therefore, this integrated waveform collaboratively supports the above three functions, enabling flexible allocation of the available AC dynamic range between the average level spacing for communication and the MLS amplitude for sensing. For simplicity, this invention uses 2-PAM as an example for formula derivation; this method can be easily extended to higher-order modulation.

[0044] Consider a bipolar MLS of length L, m[l]∈{-1,+1}, where l∈{0,1,…,L-1}. This sequence is generated by a linear feedback shift register and repeats periodically in the time domain. Its sample mean is expressed as... For finite-length MLS, this mean is typically not strictly zero. Each communication symbol spans N. rep Each MLS cycle, i.e., each PAM symbol contains N sps =N rep L sampling points. For the k-th PAM symbol (k∈{0,1,…,K-1}), its corresponding set of sampling points is defined as... .

[0045] For 2-PAM, the symbol-dependent communication level is defined as follows: , where Δ represents half of the communication level interval. Correspondingly, the sign-related term μ k Given by the following formula

[0046]

[0047] Where b k ∈{0,1} represents the emitted binary symbol.

[0048] According to μ k The AC waveform within the k-th symbol interval, along with the MLS components, can be represented as follows:

[0049]

[0050] Where S represents the amplitude assigned to the MLS component. Therefore, The sign level mean is Due to the offset introduced by MLS Maintaining constancy across symbols, the communication information still consists of the symbol-related term μ. k The carrying capacity, while the sensing function relies on the retained fast MLS transitions. Therefore, the overall waveform containing K symbols can be represented as...

[0051]

[0052] in This represents the floor function operator. A DC bias B is then superimposed to form the final transmit waveform. .

[0053] because And m[l]∈{-1,+1}, the transmitted signal samples satisfy In order to constrain peak amplitude A max To ensure non-negativity of transmission, the waveform parameters must satisfy... as well as Equivalently, the feasible communication envelope span and sensing amplitude must satisfy... This relationship indicates that the DC bias B simultaneously determines both the reference light intensity and the effective AC budget C. eff The latter is defined as

[0054]

[0055] Therefore, the choice of B essentially determines the achievable communication and sensing performance.

[0056] Within the available budget C eff Within this range, the communication level interval Δ and the sensing MLS amplitude S are jointly determined by the amplitude allocation factor ρ∈(0,1), which can be specifically expressed as follows:

[0057]

[0058] Substitute mapping relationship The final transmitted waveform segment of the k-th symbol can be rewritten as follows:

[0059]

[0060] Therefore, the overall transmission waveform containing all K symbols can be rewritten as follows:

[0061]

[0062] in .

[0063] For a sufficiently long, equally probable bit stream, the data-dependent communication terms tend to zero in expectation. Therefore, the time-averaged signal level of x[n] can be approximated as:

[0064]

[0065] Accordingly, the average DC power of the transmitted waveform is defined as .

[0066] A larger ρ allocates more AC budget to the symbol-dependent level intervals, thus benefiting communication demodulation. Conversely, a smaller ρ reserves more amplitude for fast MLS components, enhancing sensing correlation peaks. Therefore, with a fixed AC budget, ρ directly determines the trade-off between communication and sensing, while the DC bias B determines the upper limit of the feasible dynamic range. Figure 2 The time-domain structures in (a) and (b) show that increasing ρ expands the average envelope interval at the expense of MLS variability, while B establishes the overall amplitude boundary. Furthermore, Figure 2The cross-correlation characteristic curve in (c) shows that the proposed MLS-PAM mean envelope waveform produces a significant sensing peak, which is absent in pure PAM waveforms. As verified in 1(d), this correlation peak gradually decays with increasing ρ, directly reflecting the decreasing amplitude allocated to the MLS component. Compared with existing MLS-PAM waveform designs based on peak envelopes, the MLS-PAM mean envelope waveform design and resource allocation mechanism proposed in this invention can fully utilize limited amplitude resources and achieve synergistic optimization of communication, sensing, and energy harvesting performance under limited dynamic range conditions.

[0067] 2. Information Decoding Module

[0068] At the target node, the signal output from the photovoltaic receiver first passes through a power separation circuit, where the DC component is extracted for energy harvesting, while the remaining AC component is used for communication processing. After sampling, the resulting discrete-time series is represented as y. PV [n]. Due to the limited response speed of photovoltaics, communication recovery is achieved through low-rate statistics per symbol, rather than relying on chip-level waveform tracking. To this end, the receiver uses an index set... The received sample points are averaged to construct a decision statistic for each symbol interval.

[0069]

[0070] Subsequently, the equivalent channel gain and baseline offset are linearly fitted using least-squares estimation. Based on the extracted channel feature parameters, the mean of the observation blocks of all received symbols is equalized to obtain the recovered PAM symbols. Finally, by... With two permissible levels and PAM demodulation is completed through comparison. The corresponding bit decision can be represented as:

[0071]

[0072] Here, b represents the set of transmitted binary symbols; the bits detected within all symbol intervals constitute the recovered binary sequence. The performance of the communication module is quantified by the bit error rate (BER).

[0073] 3. Sensing Processing Module

[0074] Part of the transmitted beam is reflected by the target and returns to the transceiver along the same path. The returned echo is detected by the photodiode (PD) at the transceiver end and digitized into a discrete time series. Let f s This represents the sampling rate of the sensing A / D conversion. The corresponding ranging resolution is determined by the delay quantization interval, expressed as... , where c is the speed of light.

[0075] To measure the propagation delay, the transceiver constructs a local reference signal based on the transmitted MLS-PAM waveform and performs a sliding correlation operation with the received echo. Let y s [n] represents the sampled echo sequence, and let x s [n] represents the corresponding local reference signal. The time delay estimate can be obtained by identifying the hysteresis that maximizes the correlated output.

[0076]

[0077] Among them, L w For the relevant window length, This is the preset time-delay search area. The estimated target distance can then be expressed as... Perception performance is quantified using the root mean square error of ranging (RMSE).

[0078] 4. Energy Harvesting Module

[0079] After power separation, the DC component extracted from the photovoltaic output is fed into the energy harvesting module. Accordingly, the harvested energy (HE) is the average DC optical power related to the emitted waveform. The decision is made. The generated photocurrent can be expressed as...

[0080]

[0081] Among them, h e This represents the effective power channel coefficient of the energy transmission link, while (Unit: A / W) is the conversion responsivity factor, which characterizes the photoelectric conversion efficiency of a photovoltaic receiver.

[0082] The electrical characteristics of a photovoltaic receiver are described by its DC equivalent circuit model. Let I and V represent the output current and voltage at the load terminal, respectively. The corresponding I-V characteristic curve is implicitly determined by the following equation.

[0083]

[0084] Where I0 represents the reverse saturation current of the diode, R S and R SH These represent the resistance values ​​of the series resistor and the parallel resistor, respectively. The thermal voltage is given by q, where q is the electron charge. (where Boltzmann constant is used).

[0085] By connecting a load resistor at the end of the energy harvesting module (in (representing the set of candidate load resistors), the extractable electrical power is evaluated as follows: Therefore, the maximum collection power is given by the following equation. By selecting a load resistor that maximizes the extracted power, the system operates at its maximum power point, thus making full use of the incident light energy.

[0086] To clearly describe the beneficial effects of the proposed waveform design method, the following embodiment is presented through numerical simulation. This aims to verify the effectiveness of the MLS-PAM mean envelope waveform and clarify the controllable trade-off between communication, sensing, and energy harvesting achieved by the amplitude allocation factor ρ and DC bias B. The main simulation parameters are set as follows: transceiver position is (2.5, 2.5, 3), target node position is (2, 2, 1), D / A sampling rate is 500 MSa / s, and A / D sampling rate is f. s The efficiency is 2.5 GSa / s, the MLS base sequence length L is 15, and each PAM symbol lasts for N seconds. rep = 2 MLS cycles, N sampling times per PAM symbol sps The peak amplitude is 30. max The value is 1, and the length of the sliding window is L. w The photocurrent conversion coefficient η is 2000. ph The dark current I0 is 0.161 A / W, the temperature T is 300K, and the dark current I0 is 10. -10 A, series resistance R S It is 0.42×10 -3 Ω, parallel resistance R SH 5×10 3 Ω, the load resistor set is 10 0 ~10 6 Ω. Under this setting, the communication rate is 500 / (2×15)×log2(2)=16.67 Mbps. The distance resolution is 3×10⁻⁶. 8 / (2×2.5×10 9 =0.06 m.

[0087] Figure 3 The study demonstrates how communication and sensing performance varies with signal-to-noise ratio (SNR) under different waveform parameter ρ and B settings. Figure 3 (a) and Figure 3 (b) First, the effect of ρ is explained when the DC bias level B = 0.4. For example... Figure 3 As shown in (a), for all tested ρ values, the BER monotonically decreases with SNR. At any given SNR, a larger ρ consistently yields a lower BER, indicating that allocating more AC budget to communication-oriented level spacing improves the reliability of block-averaged detection at the photovoltaic receiver. This advantage is particularly pronounced in the low SNR range, where symbol detection is more sensitive to insufficient spacing between two decision levels. In contrast, Figure 3 (b) indicates that the perception performance tends to favor smaller ρ. Reserving more AC budget for the MLS components enhances the correlation peak and improves delay estimation accuracy. As SNR increases, the RMSE curve gradually approaches a similar lower bound of error, suggesting that the residual perception error is primarily constrained by the limited sampling resolution and estimator structure, rather than noise.

[0088] Figure 3 (c) and Figure 3 (d) The effect of the bias level B was evaluated when the allocation factor ρ was fixed. Figure 3 As shown in (c), the BER exhibits a monotonically improving trend with increasing B. Within the range considered, a larger B provides a greater feasible AC dynamic range, thus allowing for a wider communication level interval even with a fixed ρ. Figure 3 (d) Similar monotonic improvements are observed in sensing performance. When B is small, the permissible AC budget is severely limited, leaving only a weak MLS component, leading to larger ranging errors, especially under low SNR conditions. Increasing B enhances the permissible MLS variability and significantly improves delay estimation accuracy. In summary, these findings suggest that while the allocation factor ρ strictly divides the available AC budget between communication and sensing, the DC bias B fundamentally determines the absolute dynamic range capacity supporting both functions.

[0089] Figure 4 The dependence of BER, RMSE, and HE on ρ and B is further clarified when SNR=0 dB, thus more intuitively demonstrating the trade-offs of the waveform. Figure 4 (a) and Figure 4 (b) shows how performance varies with ρ at different bias levels. When B is small, the available AC budget is already limited, so increasing ρ quickly suppresses the MLS amplitude and causes a sharp increase in RMSE. When B is large, the system is able to maintain a low RMSE over a wider range of ρ, indicating that a sufficiently large bias not only improves the absolute performance level but also alleviates the trade-off between communication and sensing. Figure 4 (c) Further, it is shown that for a fixed B, HE varies very little with ρ, but changes much more significantly at different bias levels. This result indicates that ρ primarily regulates the balance between communication and sensing, while energy transfer capability is mainly controlled by the bias level.

[0090] Figure 4 (d)-(f) shift the focus of the analysis to fixing the allocation factor ρ and changing the DC bias B. For example... Figure 4 As shown in (d), both the communication BER and the perceived RMSE show a consistent decrease as B increases. Figure 4(e) demonstrates that the magnitude of the improvement in perception performance is heavily dependent on ρ. When ρ is large, only a very small portion of the AC budget is reserved for the perception component. Therefore, the absolute budget level is crucial, making the RMSE highly sensitive to changes in B. Furthermore, Figure 4 (f) shows that HE increases proportionally with B, while the energy dependence on ρ is negligible. Therefore, this parameter analysis highlights that the DC bias B not only extends the feasible communication-sensing operating range, but also fundamentally determines the energy transfer capability of the proposed waveform.

[0091] Numerical results validate the effectiveness and flexibility of the MLS-PAM waveform proposed in this invention. By decoupling the slow communication envelope from the fast MLS transition, this design effectively overcomes the bottleneck of photovoltaic sampling rate, achieving a unified communication, sensing, and power transfer. Under finite dynamic range constraints, system performance is jointly determined by the DC bias B and the amplitude allocation factor ρ. Specifically, B establishes the macroscopic performance boundary; increasing B alters the total AC budget, thereby simultaneously enhancing all three functions. Conversely, ρ flexibly reallocates this available budget, prioritizing either communication-oriented level spacing or sensing-oriented MLS fluctuations. Crucially, the inherent coupling of these parameters ensures that sufficient B can expand the feasible operating region, significantly mitigating the severe communication-sensing tradeoff observed under a tight AC budget. Ultimately, this framework provides a highly adaptive solution for integrated optical communication, sensing, and power transfer systems equipped with low-bandwidth target-side photovoltaic receivers.

[0092] Finally, it should be noted that the above examples of the present invention are merely illustrative and not intended to limit the implementation of the invention. Although the applicant has described the present invention in detail with reference to preferred embodiments, those skilled in the art can make other variations and modifications based on the above description. It is impossible to exhaustively list all possible implementations here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A finite dynamic range constrained transmit waveform design method for a target-side photovoltaic receiver-optical-sensing-energy integrated system, the target-side photovoltaic receiver-optical-sensing-energy integrated system comprising a sensing signal generator for generating an integrated sensing digital signal, a transmitter, a photovoltaic receiver, an information decoding module, an energy harvesting module, and a sensing processing module; the sensing signal generator and transmitter are mounted on a transceiver, and the photovoltaic receiver, information decoding module, and energy harvesting module are mounted on the target side; the sensing signal generator comprises an MLS modulation unit, a PAM modulation unit, and an envelope modulation unit, the MLS modulation unit generating the MLS signal using a linear feedback shift register based on a primitive polynomial. The PAM modulation unit modulates the input raw signal to obtain a PAM sequence, and the envelope modulation unit uses the PAM sequence as a slow envelope to perform envelope modulation on the MLS sequence to obtain an envelope-modulated AC signal. A DC bias is superimposed on the envelope-modulated AC signal to complete AC-DC coupling and obtain an analog electrical signal. The transmitter converts the analog electrical signal into an optical signal, which is simultaneously connected to the sensing and processing module and the photovoltaic receiver. The photovoltaic receiver outputs the received optical signal as an electrical signal, which is then separated into a DC signal and an AC signal by a power separation unit. The DC signal is connected to the energy harvesting module, and the AC signal is connected to the information decoding module. Its features are: The transmitted waveform is the optical signal output by the transmitter, which is formed according to the following steps. 1) First, generate an MLS sequence. Let the bipolar MLS sequence of length L be denoted as m[l]∈{-1,+1}, where l∈{0,1,…,L-1}; this sequence repeats periodically in the time domain; simultaneously, PAM modulation is applied to the original signal to obtain a PAM sequence; each communication symbol spans N. rep Each MLS sequence period contains N PAM symbols. sps =N rep L sampling points; for the k-th PAM symbol, k∈{0,1,…,K-1}, its corresponding set of sampling points is defined as ; 2) Envelope Modulation: For a second-order PAM sequence, the symbol-dependent communication level is defined as... Where Δ represents half of the communication level interval; correspondingly, the sign-related term μ k Given by the following formula Where b k ∈{0,1} represents the emitted binary symbol; According to μ k The AC waveform within the k-th symbol interval, along with the MLS components, is represented as follows: Where S represents the amplitude assigned to the MLS; then the overall AC waveform containing K symbols after envelope modulation is expressed as follows: in This represents the integer division operator; 3) Finally, a DC bias B is superimposed on the AC waveform to obtain the analog electrical signal, which is the transmitter output waveform. .

2. The finite dynamic range constrained transmit waveform design method for a target-side photovoltaic receiver optical communication and sensing integrated system according to claim 1, characterized in that: The kth transmitted signal satisfy Waveform parameters satisfy as well as A max For peak amplitude constraints; equivalently, the feasible communication envelope span and sensing amplitude must satisfy... The DC bias B simultaneously determines both the reference light intensity and the effective AC budget C. eff C eff Defined as Within the available budget C eff Within this range, the communication level interval Δ and the MLS amplitude S are jointly determined by the amplitude allocation factor ρ∈(0,1), expressed as: Substitute mapping relationship Then the final transmitted waveform of the k-th symbol is Therefore, the overall transmission waveform containing all K symbols is in .

3. The finite dynamic range constrained transmit waveform design method for a target-side photovoltaic receiver optical communication and sensing integrated system according to claim 2, characterized in that: Set the amplitude allocation factor ρ to an adjustable parameter, within a fixed AC budget C. eff The following approach balances communication and sensing through the value of ρ; the feasible upper limit of the dynamic range is determined by the DC bias B; under the constraint of finite dynamic range, the system performance is jointly determined by the DC bias B and the amplitude allocation factor ρ, where the DC bias B establishes the macroscopic performance boundary, and increasing B changes the AC budget C. eff This simultaneously alters the three functions of synesthesia; the amplitude allocation factor ρ affects the AC budget C. eff The levels are redistributed to achieve communication-oriented level spacing Δ and perception-oriented MLS amplitude S fluctuation.

4. The finite dynamic range constrained transmit waveform design method for a target-side photovoltaic receiver optical communication and sensing integrated system according to claim 2, characterized in that: For a sufficiently long, equally probable bitstream, the data-dependent communication term tends to zero in expectation; therefore, the time-averaged signal level of x[n] is approximately expressed as: Accordingly, the average DC optical power of the transmitted waveform is defined as .

5. The finite dynamic range constrained transmit waveform design method for a target-side photovoltaic receiver optical communication and energy sensing integrated system according to claim 1, characterized in that: The AC signal separated by the power separation unit is converted from analog to digital signal. After sampling, the resulting discrete-time sequence is represented as y. PV [n], the receiver uses the index set The received sample points are averaged to construct a decision statistic for each symbol interval. Subsequently, the equivalent channel gain and baseline offset were linearly fitted using least squares estimation; based on the extracted channel feature parameters, the mean of the observation blocks of all received symbols was equalized to obtain the recovered PAM symbols. Finally, by... With two permissible levels and The comparison is used to complete PAM demodulation; the corresponding bit decision is represented as follows: Where b is the set of emitted binary symbols; The bits detected within all symbol intervals constitute the recovered binary sequence, thus enabling information decoding.

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