LoRa system based on intelligent reflecting surface beamforming and index modulation and physical layer enhancement method

Abstract

This invention provides a LoRa system based on intelligent reflective surface beamforming and index modulation, and a LoRa physical layer enhancement method. In this system, the transmitter includes at least: N k A LoRa node equipped with a single transmit antenna and a RIS with N passive reflective elements. The RIS is used to improve the wireless propagation environment by controlling the phase of each passive reflective element to achieve beamforming, maximizing the instantaneous signal-to-noise ratio under the active antenna combination, and improving bit error rate performance; the receiver includes at least N... R A LoRa gateway with multiple receiving antennas, where multiple receiving antennas at the LoRa gateway are used for indexed modulation, activating N antennas in each symbol period. R N of the receiving antennas k By using these individual antennas as information transmission entities to carry additional information, the transmission throughput is significantly improved. The LoRa physical layer enhancement method applied to this LoRa system improves the quality of received signals by controlling the wireless propagation environment through RIS beamforming, and cleverly utilizes the IM principle of multi-receiver antenna indexing to improve spectral efficiency, thus achieving both high transmission rate and high reliability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a LoRa system based on intelligent reflecting surface beamforming and index modulation and a physical layer enhancement method. BACKGROUND

[0002] The increasing development and maturity of the Internet of Things (IoT) is promoting the transformation of society and leading it into a new social form under the deep application of IoT technology. The IoT in the 5G era is greatly changing people's previous business models and lifestyles by providing real-time information, analysis and decision-making, thus bringing unlimited possibilities for the future development of society. LPWAN (Low Power Wide Area Network) with communication capability under extremely low signal-to-noise ratio fills the technical gap that other transmission technologies cannot achieve long-distance low-power transmission in complex environments, and has become an important basis for supporting IoT connection. Many excellent technologies have emerged as the times require, among which LoRa (Long Range Radio) using spread spectrum technology has become one of the most promising solutions in LPWAN. LoRa uses CSS (Chirp Spread Spectrum) at the physical layer to effectively expand the coverage of the network and make it have the ability to recover from interference and fading. In addition, LoRa supports multiple spreading factors to achieve a trade-off between distance and data rate, and uses different possibilities such as carrier frequency (CF) and spreading factor (SF) to orthogonalize transmission as much as possible.

[0003] Although LoRa has a wide coverage and can change the data rate by changing the spreading factor, its maximum transmission rate is still low. Through a strict analysis of the potential bit error rate of LoRa, it is shown that in a city environment with Rayleigh fading characteristics, LoRa may not be able to maintain long-distance communication, and then transmission quality problems will occur. Worse still, the interference caused by the use of the same spreading factor in a large-scale connected LoRa network will lead to further deterioration of the performance of the LoRa system.

[0004] Currently, the research on LoRa physical layer enhancement mainly proceeds from two aspects of improving the LoRa transmission rate and reducing the influence of fading channels on LoRa:

[0005] In one aspect, in order to improve the transmission rate of LoRa to be applicable to applications with larger data throughput, in the document “Frequency-Shift Chirp Spread Spectrum Communications With Index Modulation”, a system called FSCSSIM is proposed, which combines LoRa with IM (Index Modulation) to represent messages by using a combination of orthogonal chirp signals. As to how to incorporate IM into LoRa modulation as an ideal solution to improve the data rate of LoRa modulation, the document “A New Index Modulation for LoRa” proposes a new index modulation, which is to send multiple quasi-orthogonal chirps modulated at different spreading factors simultaneously, thereby utilizing the SF domain as a means to carry more information.

[0006] On the other hand, in order to solve the problem that the performance of the LoRa system will be severely deteriorated in the fading channel environment, in the document “Design and Performance Analysis of a New STBC-MIMO LoRa System”, a multiple-input multiple-output configuration using a space-time block coding (STBC) scheme is introduced into the LoRa system to develop a STBC-MIMO LoRa system, and the theoretical performance of the proposed system in a Rayleigh fading channel is studied. Jae-Mo Kang proposes a new integration of MIMO and LoRa systems for high data rate IoT, and designs a receive signal-to-noise ratio maximization precoding to balance data rate and link reliability. In the document “A New Reconfigurable Intelligent-Surface-Assisted LoRa System”, the intelligent reflecting surface is used as another promising solution in the LoRa system, which uses M-ary differential phase-shift keying to generate the reflection coefficients of RIS (Reconfigurable Intelligent Surface) to reduce the impact of fading channels while transmitting additional information.

[0007] In the related art, although the IM and RIS technologies have shown good performance improvement effect in their respective application fields, there is no research on how to intelligently combine and apply these two technologies to LoRa modulation in order to optimize its transmission throughput and error code performance at the same time. SUMMARY

[0008] In order to solve the above problems existing in the prior art, the application provides a LoRa system based on intelligent reflecting surface beamforming and index modulation and a physical layer enhancement method.

[0009] In a first aspect, the application provides a LoRa system based on intelligent reflecting surface beamforming and index modulation, characterized in that it comprises a transmitting party and a receiving party, wherein the transmitting party at least comprises N k LoRa nodes equipped with a single transmitting antenna and an intelligent reflecting surface RIS with N R passive reflecting elements, and the receiving party at least comprises a LoRa gateway equipped with N R receiving antennas; wherein

[0010] The LoRa nodes are configured to generate LoRa linear spread spectrum modulation signals.

[0011] The intelligent reflecting surface RIS is configured to maximize the instantaneous signal-to-noise ratio of the activated receiving antenna combination by controlling the phases of different passive reflecting elements for beamforming.

[0012] The LoRa gateway is configured to activate N k out of N R receiving antennas for index modulation in each transmission period.

[0013] In a second aspect, the application provides a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation, which is applied to the transmitting party of the LoRa system in the first aspect, and the method comprises the following steps:

[0014] Converting serial information bits to be transmitted in the current symbol period into parallel information bits, wherein the parallel information bits include one modulated bit and one index bit;

[0015] Generating an effective active antenna combination matrix set for N k out of N k receiving antennas activated in each symbol period;

[0016] Converting the index bit into an index symbol and selecting an active antenna combination from the effective active antenna combination matrix set according to the index symbol;

[0017] Configuring the phases of each passive reflecting element in the intelligent reflecting surface according to the active antenna combination to maximize the instantaneous signal-to-noise ratio under the active antenna combination;

[0018] Converting the modulated bit into a modulated symbol and performing LoRa linear spread spectrum modulation on the modulated symbol to obtain N kThe spread spectrum modulated signal of a LoRa node is transmitted through N k The spread spectrum modulated signal of a LoRa node is transmitted through N

[0019] In an embodiment of the present application, the step of generating an effective active antenna combination matrix set from N R In an embodiment of the present application, the step of generating an effective active antenna combination matrix set from N k The step of generating an effective active antenna combination matrix set from N

[0020] In an embodiment of the present application, the step of generating an effective active antenna combination matrix set from N R In an embodiment of the present application, the step of generating an effective active antenna combination matrix set from N k There are Q possible active antenna combinations in total, wherein There are Q possible active antenna combinations in total, wherein

[0021] The active antenna combinations with Q active antenna combinations are regarded as legal active antenna combinations, and the rest are regarded as illegal active antenna combinations, and an effective active antenna combination matrix set is generated Wherein, A i represents the i-th of the Q effective active antenna combinations,

[0022] In an embodiment of the present application, the step of maximizing the instantaneous received signal-to-noise ratio under the active antenna combination by configuring the phase of each passive reflecting element in the intelligent reflecting surface according to the active antenna combination, comprises:

[0023] The intelligent reflecting surface is equally divided into N k RIS sub-blocks, and each RIS sub-block configures the phase of its passive reflecting elements according to the receiving antenna it serves to achieve the maximum instantaneous received signal-to-noise ratio; wherein the number of passive reflecting elements in each RIS sub-block is

[0024] In a third aspect, the present application also provides a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation, applied to the receiver of the LoRa system of the first aspect, the method comprising:

[0025] Obtaining a received signal through multiple receiving antennas and generating a received signal matrix;

[0026] Sorting the signals in the received signal matrix according to the intensity to determine the receiving antenna most likely to be activated;

[0027] Calculating the probability of occurrence of each of the receiving antennas most likely to be activated;

[0028] calculate the occurrence probability of all active antenna combinations containing the most likely activated receiving antennas according to the occurrence probability of each of the most likely activated receiving antennas;

[0029] sort according to the occurrence probability of the active antenna combinations to obtain a set of possibility-sorted legal active antenna combinations C s select the first Λ legal active antenna combinations with the largest occurrence probability in C s to reduce the solution space;

[0030] obtain the index symbol estimation value of the active antenna combination and the LoRa symbol estimation value through Λ iterations;

[0031] generate a bit stream and output based on the index symbol estimation value and the LoRa symbol estimation value.

[0032] In an embodiment of the present application, the step of determining the most likely activated receiving antennas according to the strength of the signals in the receiving signal matrix comprises:

[0033] take the absolute value of each column element in the receiving signal matrix and sort to obtain the index of the first elements with the largest absolute value in each column, and generate an index set;

[0034] count the number of occurrences of each index in the index set;

[0035] determine the receiving antenna corresponding to the index with the largest number of occurrences as the most likely activated receiving antenna.

[0036] In an embodiment of the present application, the step of calculating the occurrence probability of each of the most likely activated receiving antennas comprises:

[0037] calculate the total number of occurrences of all indexes in the index set;

[0038] calculate the occurrence probability of each receiving antenna by calculating the ratio of the number of occurrences of each index in the index set to the total number of occurrences.

[0039] In an embodiment of the present application, the step of calculating the occurrence probability of all active antenna combinations containing the most likely activated receiving antennas according to the occurrence probability of each of the most likely activated receiving antennas comprises:

[0040] calculate the product of the occurrence probabilities of the N k receiving antennas in each active antenna combination to obtain the occurrence probability of each of the active antenna combinations.

[0041] In one embodiment of the present invention, the active antenna combinations are sorted according to their probability of occurrence to obtain a set C of legal active antenna combinations ordered by probability. s Select C s The steps for narrowing the solution space by selecting the top Λ legally active antenna combinations with the highest probability include:

[0042] The active antenna combinations are sorted according to their occurrence probability to obtain a set of active antenna combinations sorted by occurrence probability.

[0043] Examine the set of active antenna combinations sorted by occurrence probability, remove illegal active antenna combinations, and obtain the set of legal active antenna combinations C sorted by probability. s ;

[0044] From the set C of legally active antenna combinations ordered by probability s The Λ most likely combinations are selected as the number of active antenna combinations to be traversed, in order to reduce the solution space.

[0045] In one embodiment of the present invention, the step of obtaining the index symbol estimate and LoRa symbol estimate of the activated antenna combination through Λ iterations includes:

[0046] Let u=1;

[0047] Determine whether u≤Λ is satisfied;

[0048] When u≤Λ, the uth effective active antenna combination among the first Λ effective active antenna combinations is taken as the current receiving antenna combination;

[0049] Calculate the phase vector of the smart reflective surface corresponding to the current receiving antenna combination;

[0050] Based on the current receiving antenna combination, the superimposed signal of the corresponding row is selected from the received signal matrix, and the superimposed signal is ZF equalized and LoRa incoherently demodulated using the intelligent reflective surface phase vector to obtain the strongest node signal.

[0051] Let v = 2;

[0052] Determine if v≤N k If so, then the strongest node signal is used for interference cancellation, and the solved second strongest node signal is subjected to ZF equalization and LoRa incoherent demodulation. After further setting v = v + 1, the process returns to the judgment of whether v ≤ N. k Steps;

[0053] If not, then calculate the F-norm of the difference between the received signal and the predicted signal; the predicted signal is: the channel matrix, the smart reflector phase vector corresponding to the current receiving antenna combination, and the estimated N. k The product of the superimposed signal vectors reconstructed from the LoRa modulated symbols of each node;

[0054] Determine whether the F-norm of the difference between the received signal and the predicted signal is less than a preset threshold; if so, update the F-norm of the difference between the received signal and the predicted signal, the index symbol estimate of the active antenna combination obtained in the previous round, and the LoRa symbol estimate, and further set u = u + 1, and return to the step of determining whether u ≤ Λ is satisfied;

[0055] When u>Λ, output the index symbol estimate and LoRa symbol estimate of the current active antenna combination.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] This invention provides a LoRa system based on intelligent reflective surface beamforming and index modulation. The system includes a transmitter and a receiver, and the transmitter includes at least N. k A LoRa node equipped with a single transmit antenna and a RIS with N passive reflective elements. The RIS is used to improve the wireless propagation environment by controlling the phase of different passive reflective elements to achieve beamforming, maximizing the instantaneous signal-to-noise ratio under a specific active antenna combination, thereby improving bit error rate performance. Furthermore, the receiver includes at least: a LoRa node equipped with N... R A LoRa gateway with multiple receiving antennas, where multiple receiving antennas at the LoRa gateway are used for indexed modulation, activating N antennas in each symbol period. R N of the receiving antennas k This allows the entity to carry additional information, thereby significantly improving transmission throughput.

[0058] This invention also provides a LoRa physical layer enhancement method based on intelligent reflective surfaces and indexed modulation for the transmitter. It intelligently combines RIS (Radio Reflection Surface) and IM (Index Modulation) in the LoRa system. This not only improves the quality of received signals by controlling the wireless propagation environment through RIS beamforming but also cleverly utilizes the IM principle of multi-receiver antenna indexing to improve spectral efficiency, achieving both high transmission rate and high reliability. Furthermore, this invention extends this LoRa physical layer enhancement to scenarios where multiple LoRa nodes use the same spreading factor for parallel transmission. This effectively solves the severe packet collision problem commonly found in LoRa networks caused by large-scale connections, avoids channel resource waste caused by data corruption and packet loss during transmission, saves energy, and extends the lifespan of low-power IoT nodes.

[0059] The application will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a structural block diagram of a LoRa system based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application.

[0061] Figure 2 is another structural block diagram of a LoRa system based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application.

[0062] Figure 3 is a transmitter flowchart of a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application.

[0063] Figure 4 is a flowchart of a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application.

[0064] Figure 5 is a receiver flowchart of a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application.

[0065] Figure 6 is a comparison diagram of bit error rate performance under different numbers of intelligent reflecting surface elements N and spreading factors SF provided by an embodiment of the application.

[0066] Figure 7 is a comparison diagram of throughput performance under different numbers of intelligent reflecting surface elements N and spreading factors SF provided by an embodiment of the application. DETAILED DESCRIPTION

[0067] The application will be further described in detail below with reference to the accompanying drawings and embodiments, but the embodiments of the application are not limited thereto.

[0068] Figures 1-2 is a structural block diagram of a LoRa system based on intelligent reflecting surface beamforming and index modulation provided by an embodiment of the application. As shown in Figures 1-2 , the application provides a LoRa system based on intelligent reflecting surface beamforming and index modulation, which includes a transmitter and a receiver. The transmitter at least includes N k LoRa nodes equipped with a single transmitting antenna and an intelligent reflecting surface RIS with N R passive reflecting elements, and the receiver at least includes a LoRa gateway equipped with N

[0069] LoRa nodes, for generating LoRa linear spread spectrum modulation signals;

[0070] a smart reflecting surface RIS for beamforming by controlling the phase of different passive reflecting elements to maximize the instantaneous signal-to-noise ratio of the active receiving antenna combination;

[0071] a LoRa gateway for index modulation by activating N R out of N k receiving antennas in each transmission period.

[0072] Specifically, the above LoRa system consists of N k LoRa nodes equipped with a single antenna, one LoRa gateway equipped with N R antennas, and a smart reflecting surface with N passive reflecting elements. Considering the scenario of parallel transmission of multiple LoRa nodes using the same spreading factor, the LoRa transmission is assisted by the smart reflecting surface and index modulation. The smart reflecting surface RIS is very close to the LoRa nodes and serves as part of the transmitting party, which can provide adjustable phase shifts to replace the traditional beamforming process. The receiving antennas at the LoRa gateway are used for index modulation, activating N R out of N k receiving antennas in each symbol period, which are used as information transmission entities to carry additional information.

[0073] In the case where the LoRa nodes are close enough to the RIS, their transmission is not affected by fading. The wireless channel matrix between the LoRa gateway and the RIS is denoted as where the element represents the channel between the lth antenna of the gateway and the nth reflecting element of the RIS. Under the assumption of uncorrelated flat Rayleigh fading, follows an independent complex Gaussian distribution, where l = 1, 2,..., N R , n = 1, 2,..., N. The phase vector of the passive reflecting elements of the RIS is denoted as

[0074]

[0075] Figure 3 is a transmitting party flowchart of a LoRa physical layer enhancement method based on smart reflecting surface beamforming and index modulation provided by an embodiment of the present application, Figure 4 is a flowchart of a LoRa physical layer enhancement method based on smart reflecting surface beamforming and index modulation provided by an embodiment of the present application. As Figures 3-4 shown, the present application provides a LoRa physical layer enhancement method based on smart reflecting surface beamforming and index modulation, applied to the transmitting party of a LoRa system, which comprises the following steps:

[0076] S101, converting serial information bits to be transmitted in a current symbol period into parallel information bits, the parallel information bits including one modulation bit and one index bit;

[0077] S102, generating an effective active antenna combination matrix set for N R active antennas in each symbol period; k

[0078] S103, converting the index bit into an index symbol, and selecting an active antenna combination from the effective active antenna combination matrix set according to the index symbol;

[0079] S104, configuring phases of each passive reflecting element in the intelligent reflecting surface according to the active antenna combination, and maximizing an instantaneous received signal-to-noise ratio under the active antenna combination;

[0080] S105, converting the modulation bit into a modulation symbol, and performing LoRa linear spread spectrum modulation on the modulation symbol to obtain spread spectrum modulation signals of N k LoRa nodes, which are transmitted by the N k LoRa nodes, superimposed on the phase-configured intelligent reflecting surface and reflected to a receiving party, received and demodulated by the receiving party.

[0081] Optionally, the LoRa system further includes a serial-to-parallel converter, and the step of converting serial information bits to be transmitted in a current symbol period into parallel information bits in step S101 includes:

[0082] The serial-to-parallel converter is used to convert serial information bits to be transmitted in a current symbol period into two parallel information bits:

[0083] The first one is a modulation bit, which is modulated into linear spread spectrum modulation signals of N k LoRa nodes, and is represented as L1=N k log2M, where M=2 SF is the number of chips after spreading. The other one is an index bit, which is used to select an active receiving antenna combination, and is represented as Therefore, the number of transmittable bits in one LoRa symbol period is represented as:

[0084]

[0085] Further, the bit transmission rate is represented as:

[0086]

[0087] where SF is the N k ​The spreading factor currently used by each LoRa node B is the symbol period, and B is the LoRa transmission bandwidth.

[0088] Optionally, in step S102, N activated within each symbol period R N of the receiving antennas k The steps for generating a valid set of active antenna combination matrices include:

[0089] S1021, Activate N within each symbol period R N of the receiving antennas k There are , a total of Possible combinations of active antennas, among which,

[0090] S1022. Among the possible active antenna combinations, the active antenna combinations with the number of active antenna combinations being Q are considered valid active antenna combinations, and the rest are considered invalid active antenna combinations, generating a valid active antenna combination matrix set. Among them, A i This represents the i-th of the Q effective combinations of active antennas.

[0091] In this embodiment, the LoRa gateway has N R There are N receiving antennas, and the number of receiving antennas active at each time is N. k Then all possible combinations of active antennas are kind, Denotes the binomial coefficient. Because binary bit information is modulated, the number of active receiving antenna combinations must be an integer power of 2. In other words, the effective number of active antenna combinations is... This indicates rounding down. Therefore, in step S1022, Q receiving antenna combinations can be randomly selected from all possible active antenna combinations as valid active antenna combinations, and the remaining active antenna combinations are considered invalid. All valid active antenna combinations constitute a valid active antenna combination matrix set.

[0092] Optionally, step S104, which involves maximizing the instantaneous received signal-to-noise ratio under the activated antenna combination by configuring the phase of each passive reflective element in the intelligent reflective surface according to the activated antenna combination, includes:

[0093] Divide the smart reflective surface into N equal parts kRIS sub-blocks, each of which configures its passive reflecting elements according to the receive antenna it serves to achieve maximum instantaneous received signal-to-noise ratio; wherein the number of passive reflecting elements in each RIS sub-block is

[0094] Specifically, in each symbol period, the LoRa gateway has N k receive antennas activated, and thus needs to divide the RIS into N k parts to achieve multi-directional passive beamforming, then the RIS dedicated to each selected receive antenna is called an RIS sub-block, and the number of its elements is Exemplarily, the selected receive antenna combination index is q, q = 1, 2,..., Q, then the target channel determined by the receive antenna combination index can be expressed as wherein, Finally, the phase vector of the reflecting element of the RIS allocated to the kth activated antenna according to the received receive antenna combination index q is expressed as:

[0095]

[0096] wherein k = 1, 2,..., N k .

[0097] Further, in step S105, the modulation bit to modulation symbol conversion is completed by using the bit / symbol converter in the LoRa system, and then the LoRa linear spread spectrum modulation is performed by the LoRa modulator.

[0098] Under the channel access mechanism of the ALOHA protocol, N k LoRa nodes select the same spreading factor at the same time to transmit in the same frequency channel, and when communicating with the LoRa gateway equipped with N R antennas, the spread spectrum modulated signals of the N k LoRa nodes are transmitted by the N k LoRa nodes, superimposed on the phase-configured intelligent reflecting surface and reflected to the receiving LoRa gateway.

[0099] The received signal at the LoRa gateway is expressed as:

[0100] Y = Gθ q x p + Z;

[0101] wherein x p is the superimposed signal of the N k LoRa nodes with the same spreading factor, α i represents the transmission power allocation coefficient of the ith LoRa node, represents the LoRa discrete-time signal transmitted by the ith LoRa node at the mth sampling moment, Z is an additive white Gaussian noise (AWGN) sample at the LoRa gateway, and obeys a complex Gaussian distribution.

[0102] After the receiver receives the received signal, the received signal is demodulated.

[0103] Figure 5 is a receiver flowchart of the LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation provided by the embodiments of the present application. Please refer to Figures 4-5 The embodiments of the present application also provide a LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation, applied to a receiver of a LoRa system, comprising:

[0104] S201, obtaining a received signal through multiple receiving antennas and generating a received signal matrix;

[0105] S202, sorting the signals in the received signal matrix according to the intensity, and determining the receiving antennas most likely to be activated;

[0106] S203, calculating the appearance probability of each receiving antenna most likely to be activated;

[0107] S204, calculating the appearance probability of all activated antenna combinations containing the receiving antennas most likely to be activated according to the appearance probability of each receiving antenna most likely to be activated;

[0108] S205, sorting the activated antenna combinations according to the appearance probability to obtain a set C of legal activated antenna combinations sorted by possibility s , and selecting the first Λ legal activated antenna combinations with the largest appearance probability in C s to reduce the solution space;

[0109] S206, obtaining the index symbol estimation value and the LoRa symbol estimation value of the activated antenna combination through Λ iterations;

[0110] S207, generating a bit stream and outputting based on the index symbol estimation value and the LoRa symbol estimation value.

[0111] Based on the transmitter modulation process, the embodiments of the present application propose a corresponding maximum likelihood (ML) detection algorithm as the upper limit of the optimal performance, which selects the vector with the smallest Euclidean distance from the received signal vector as the decision output from all possible activated antenna combination indexes and LoRa symbols in all possible transmitted signal vectors, that is:

[0112]

[0113] wherein, and are the estimated values of the index of the active antenna combination and the estimated value of the LoRa symbol, respectively, and p and q are the index of all possible active antenna combinations and the LoRa symbol, respectively.

[0114] However, considering the characteristics of the exhaustive search, the computational complexity of the ML algorithm will increase sharply with the increase of the number of receiving antennas and the number of LoRa spreading factors, which is difficult to apply in practice. Therefore, the embodiment of the present application adopts a low-complexity two-stage sequential detection algorithm, that is, the first stage completes the reduction of the solution space of the active receiving antenna combination, and the second stage obtains the phase vector of the intelligent reflecting surface according to the receiving antenna combination index in the reduced solution space, and performs superimposed LoRa-like signal decoding based on the successive interference cancellation (SIC) algorithm.

[0115] In step S202, the signals in the received signal matrix are sorted according to the strength to determine the receiving antenna that is most likely to be activated, including:

[0116] S2021, taking the absolute value of each column element in the received signal matrix and sorting, obtaining the index of the first elements with the largest absolute value in each column, generating an index set;

[0117] S2022, counting the number of occurrences of each index in the index set;

[0118] S2023, determining the receiving antenna corresponding to the index with the most occurrences as the receiving antenna that is most likely to be activated.

[0119] Optionally, step S203, calculating the occurrence probability of each receiving antenna that is most likely to be activated, including:

[0120] S2031, calculating the total number of occurrences of all indexes in the index set;

[0121] S2032, calculating the occurrence probability of each receiving antenna by calculating the ratio of the number of occurrences of each index in the index set to the total number.

[0122] Further, in step S204, according to the occurrence probability of each receiving antenna that is most likely to be activated, the occurrence probability of all active antenna combinations containing the receiving antenna that is most likely to be activated is calculated, including:

[0123] calculating the product of the occurrence probabilities of the N k receiving antennas in each active antenna combination to obtain the occurrence probability of each active antenna combination.

[0124] Here, the occurrence probability of each active antenna combination is: the product of the occurrence probabilities of the Nk the product of the occurrence probabilities of the individual receive antennas.

[0125] Optionally, in step S205, the legal active antenna combination set C is sorted according to the occurrence probability of the active antenna combination, to obtain a possibility-sorted legal active antenna combination set C s , the step of selecting the top Λ active antenna combinations in C s to reduce the solution space, includes:

[0126] S2051, sort the active antenna combination set according to the occurrence probability of the active antenna combination, to obtain an occurrence-probability-sorted active antenna combination set;

[0127] S2052, check the occurrence-probability-sorted active antenna combination set, and delete the illegal active antenna combinations therefrom, to obtain a possibility-sorted legal active antenna combination set C s ;

[0128] S2053, select the top Λ active antenna combinations in the possibility-sorted legal active antenna combination set C s as the active antenna combinations to be traversed, to reduce the solution space.

[0129] Further, in step S206, the step of obtaining the index symbol estimate value of the active antenna combination and the LoRa symbol estimate value through Λ iterations, includes:

[0130] Let u = 1;

[0131] Determine whether u ≤ Λ is satisfied;

[0132] When u ≤ Λ, the u-th active antenna combination in the previous Λ active antenna combinations is taken as the current receive antenna combination;

[0133] Calculate the intelligent reflecting surface phase vector corresponding to the current receive antenna combination;

[0134] Based on the current receive antenna combination, select the corresponding row of the superimposed signal from the received signal matrix, and perform ZF equalization and LoRa non-coherent demodulation on the superimposed signal using the intelligent reflecting surface phase vector, to obtain the strongest node signal;

[0135] Let v = 2;

[0136] Determine whether v ≤ N k is satisfied; if so, perform interference cancellation using the strongest node signal, and perform ZF equalization and LoRa non-coherent demodulation on the second strongest node signal, and then let v = v + 1 and return to the step of determining whether v ≤ N k is satisfied;

[0137] If not, calculate the F-norm of the difference between the received signal and the predicted signal; the predicted signal is the product of the channel matrix, the smart reflecting surface phase vector corresponding to the current receiving antenna combination, and the LoRa modulation symbol reconstruction of the estimated N k nodes;

[0138] Determine whether the F-norm of the difference between the received signal and the predicted signal is less than a preset threshold; if yes, update the F-norm of the difference between the received signal and the predicted signal, the index symbol estimate value of the active antenna combination obtained in the last round of estimation, and the LoRa symbol estimate value, and then return to the step of determining whether u≤Λ is satisfied after u is further set to u+1;

[0139] When u>Λ, output the current index symbol estimate value of the active antenna combination and the LoRa symbol estimate value.

[0140] Specifically, Λ is set as the number of iteration cycles, and in each iteration cycle, the target channel determined by the current receiving antenna combination is represented as Correspondingly, the phase vector of the smart reflecting surface allocated to the kth active receiving antenna is represented as:

[0141]

[0142] After obtaining the phase vector of the smart reflecting surface, a successive interference cancellation algorithm is applied to the decoding of the superimposed LoRa-Like signal received at the LoRa gateway. The first step is to identify the strongest signal and decode the strongest signal information: first, apply ZF to linear equalization to the received signal, and then directly demodulate the strongest signal. The LoRa non-correlation demodulation step is: first, despread, then apply DFT to the despread signal, and then estimate the LoRa symbol by selecting the index of the frequency bit with the largest amplitude. In addition to the first step, interference cancellation is performed using the decoded LoRa symbol before each step of detection. Therefore, for the secondary strong node signal, the received signal is still equalized, then the reconstructed strongest node signal is subtracted from the received signal, and finally the LoRa symbol to be solved is non-correlation demodulated. Repeat the above operation until the LoRa symbols of N k nodes are all solved.

[0143] Finally, the F-norm between the received signal and the predicted signal is calculated Among the solutions of the Λ active receiving antenna combinations, the solution with the smallest F-norm is selected as the final estimation result. At this point, the index symbol of the active antenna combination and the LoRa symbol k from N nodes have all been detected.

[0144] Further, in step S207, the LoRa system further comprises a symbol / bit converter and a parallel / serial converter;

[0145] The step of generating a bit stream and outputting based on the index symbol estimation value and the LoRa symbol estimation value comprises:

[0146] The symbol / bit converter is used to convert the LoRa symbol estimation value into a modulation bit estimation value and convert the index symbol estimation value into an index bit estimation value;

[0147] The parallel / serial converter is used to convert the modulation bit estimation value and the index bit estimation value into a bit stream and then output.

[0148] Next, the LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation applied to the sender and the receiver is further described through simulation experiments.

[0149] Specifically, numerical simulation experiments are performed using MATLAB simulation software. In the simulation experiments, numerical analysis results of the LoRa physical layer enhancement method based on intelligent reflecting surface beamforming and index modulation are given, including bit error rate performance and throughput performance, wherein the throughput is defined as the number of bits successfully transmitted and correctly received by the receiving end per unit time. Optionally, the number of LoRa nodes is set to 2, the spreading factor commonly used by the LoRa nodes is set to 7, the LoRa transmission bandwidth is set to 250 kHz, the number of receiving antennas at the LoRa gateway is set to N R = 6, and the number of activated receiving antennas is set to N k = 2. Under the conditions of the number of intelligent reflecting surface elements N = 64, 128 and SF = 7, 8, 9, 10, the bit error rate and throughput performance of the scheme are shown.

[0150] Figure 6 is a comparison chart of the bit error rate performance under different numbers of intelligent reflecting surface elements N and spreading factors SF provided by the embodiments of the present application. From Figure 6It can be seen that as the number of smart reflective surface elements N increases from 64 to 128, the bit error rate of the LoRa system is significantly reduced, and the bit error rate performance is significantly improved, that is, increasing the number of smart reflective surface elements N is as helpful as increasing the spreading factor SF in improving the bit error rate performance of the system. This is because the smart reflective surface dynamically changes the wireless channel by intelligently controlling the reflection characteristics of the passive reflection elements, thereby improving the signal quality at the receiving end. In traditional LoRa, a larger spreading factor is usually used to achieve longer communication distance and stronger anti-interference capability. However, a larger SF will significantly increase the symbol duration, thereby reducing the data transmission rate. Increasing the number of smart reflective surface elements can achieve the same effect as increasing the SF, making the system more robust in challenging wireless environments without increasing the symbol duration, thereby better achieving the trade-off between communication distance and data rate.

[0151] Figure 7 is a comparison chart of throughput performance provided by embodiments of the present application under different numbers of smart reflective surface elements N and spreading factors SF. From Figure 7 It can be seen that when N R is 6, N k is 2, the maximum throughput of the LoRa system can reach 3.3x10 4 b / s, which is significantly improved compared with the maximum throughput 2.7x10 4 b / s that can be achieved by a traditional LoRa system using the same spreading factor under the same number of nodes. At the same time, the larger the number of smart reflective surface elements N, the earlier the throughput growth begins, and the faster the maximum throughput is reached. However, when the spreading factor SF reaches a certain level, the effect of the number of smart reflective surface elements N on the speed of reaching the maximum throughput becomes less significant. This is because a larger spreading factor brings a larger spectrum widening, providing strong anti-interference capability. At this time, the further optimization of the smart reflective surface has limited effect on the speed of achieving the maximum throughput.

[0152] From the above embodiments, the beneficial effects of the present application are as follows:

[0153] The present application provides a LoRa system based on smart reflective surface beamforming and index modulation, which includes a transmitting party and a receiving party. The transmitting party at least includes N k LoRa nodes equipped with a single transmitting antenna and an RIS with N passive reflection elements. The RIS is used to improve the wireless propagation environment, and beamforming is achieved by controlling the phase of different passive reflection elements, thereby maximizing the instantaneous signal-to-noise ratio under a certain active antenna combination and improving the bit error performance. Further, the receiving party at least includes N Ra LoRa gateway with multiple receive antennas, the multiple receive antennas at the LoRa gateway are used for index modulation, and N R of the N k receive antennas are activated in each symbol period, which is used as an information transmission entity to carry additional information, thereby significantly improving the transmission throughput.

[0154] The application provides a LoRa physical layer enhancement method based on intelligent reflecting surface and index modulation applied to a transmitting side, which intelligently combines RIS and IM and applies them to a LoRa system, not only controls a wireless propagation environment through RIS beamforming to improve the quality of a received signal, but also ingeniously utilizes the IM principle of multiple receive antennas to improve the spectral efficiency, so that the LoRa system has high transmission rate and high reliability. In addition, the application extends the LoRa physical layer enhancement to a scenario in which multiple LoRa nodes use the same spreading factor for parallel transmission, and can effectively solve the serious data packet collision problem existing in a LoRa network caused by large-scale connection, avoid the waste of channel resources caused by data damage and packet loss in the transmission process, save energy consumption, and improve the service life of a low-power Internet of Things node.

[0155] In the description of the application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the description, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in the specification.

[0156] Although the application is described herein in connection with various embodiments, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art with reference to the attached drawings, disclosure, and appended claims in implementing the claimed application.

[0157] The above is a further detailed description of the application in combination with specific preferred embodiments, and the specific implementation of the application cannot be limited to these descriptions. For those skilled in the art, without departing from the concept of the application, a number of simple deductions or replacements can be made, which should be considered as falling within the protection scope of the application.

Claims

1. A LoRa system based on intelligent reflective surface beamforming and index modulation, characterized in that, include: The transmitter and receiver, wherein the transmitter includes at least: N k A LoRa node equipped with a single transmitting antenna and a smart reflective surface RIS with N passive reflective elements, wherein the receiver includes at least: equipped with N R A LoRa gateway with one receiving antenna; among which... The LoRa node is used to generate a LoRa linear spread spectrum modulation signal; The intelligent reflective surface RIS is used to perform beamforming by controlling the phase of different passive reflective elements, thereby maximizing the instantaneous signal-to-noise ratio of the activated receiving antenna combination. The LoRa gateway is used to activate N in each transmission cycle. R N of the receiving antennas k One is indexed and modulated.

2. A LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation, characterized in that, The method, applied to the transmitter of the LoRa system according to claim 1, comprises: The serial information bits to be transmitted in the current symbol period are converted into parallel information bits, which include one modulation bit and one index bit. N activated within each symbol period R N of the receiving antennas k A set of valid active antenna combination matrices is generated; The index bits are converted into index symbols, and active antenna combinations are selected from the set of valid active antenna combination matrices according to the index symbols. The instantaneous received signal-to-noise ratio under the activated antenna combination is maximized by configuring the phase of each passive reflective element in the intelligent reflective surface according to the activated antenna combination. The modulation bits are converted into modulation symbols, and the modulation symbols are subjected to LoRa linear spread spectrum modulation to obtain N. k The spread spectrum modulation signal of each LoRa node is transmitted through N k Each LoRa node transmits data, which is superimposed on a smart reflective surface configured with phase and reflected to the receiver, where it is received and demodulated.

3. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 2, characterized in that, N activated within each symbol period R N of the receiving antennas k The steps for generating a valid set of active antenna combination matrices include: Activate N in each symbol period R N of the receiving antennas k There are , a total of Possible combinations of active antennas, among which, Among the possible active antenna combinations, those with an activation antenna combination count of Q are considered valid active antenna combinations, and the rest are considered invalid active antenna combinations, thus generating a valid active antenna combination matrix set. Among them, A i This represents the i-th of the Q effective combinations of active antennas.

4. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 2, characterized in that, The step of maximizing the instantaneous received signal-to-noise ratio under the activated antenna combination by configuring the phase of each passive reflective element in the intelligent reflective surface according to the activated antenna combination includes: The intelligent reflective surface is divided into N equal parts. k Each RIS sub-block configures the phase of its passive reflective elements according to the receiving antenna it serves, thereby maximizing the instantaneous receive signal-to-noise ratio; wherein the number of passive reflective elements in each RIS sub-block is...

5. A LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation, characterized in that, The method, applied to the receiver of the LoRa system according to claim 1, comprises: The received signal is obtained through multiple receiving antennas, and a received signal matrix is ​​generated; The signals in the received signal matrix are sorted by intensity to determine the receiving antennas most likely to be activated; Calculate the probability of occurrence for each of the most likely activated receiving antennas; Based on the probability of occurrence of each of the most likely activated receiving antennas, calculate the probability of occurrence of all combinations of activated antennas that include the most likely activated receiving antennas; The active antenna combinations are sorted according to their probability of occurrence to obtain a set C of legal active antenna combinations ordered by probability. s Select C s The top Λ legal active antenna combinations with the highest probability of occurrence are used to narrow the solution space; The index symbol estimate and LoRa symbol estimate of the activated antenna combination are obtained through Λ iterations; Based on the index symbol estimate and the LoRa symbol estimate, a bit stream is generated and output.

6. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 5, characterized in that, The step of sorting the signals in the received signal matrix by intensity and determining the receiving antenna most likely to be activated includes: Take the absolute value of each column element in the received signal matrix and sort them. Obtain the element with the largest absolute value in each column. Generate an index set from the indices of the elements; Count the number of times each index appears in the index set; The receiving antenna corresponding to the index that appears most frequently is determined as the receiving antenna most likely to be activated.

7. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 6, characterized in that, The step of calculating the probability of occurrence of each of the most likely activated receiving antennas includes: Calculate the total number of occurrences of all indices in the index set; The probability of occurrence of each receiving antenna is calculated by dividing the number of occurrences of each index in the index set by the total number of occurrences.

8. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 7, characterized in that, The step of calculating the probability of occurrence of all combinations of active antennas including the most likely activated receiving antennas, based on the probability of occurrence of each of the most likely activated receiving antennas, includes: Calculate N for each active antenna combination k The probability of occurrence of each active antenna combination is obtained by multiplying the occurrence probabilities of each receiving antenna.

9. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 5, characterized in that, The active antenna combinations are sorted according to their probability of occurrence to obtain a set C of legal active antenna combinations ordered by probability. s Select C s The steps for narrowing the solution space by selecting the top Λ legally active antenna combinations with the highest probability include: The active antenna combinations are sorted according to their occurrence probability to obtain a set of active antenna combinations sorted by occurrence probability. Examine the set of active antenna combinations sorted by occurrence probability, remove illegal active antenna combinations, and obtain the set of legal active antenna combinations C sorted by probability. s ; From the set C of legally active antenna combinations ordered by probability s The Λ most likely combinations are selected as the number of active antenna combinations to be traversed, in order to reduce the solution space.

10. The LoRa physical layer enhancement method based on intelligent reflective surface beamforming and index modulation according to claim 6, characterized in that, The steps for obtaining the index symbol estimate and LoRa symbol estimate of the activated antenna combination through Λ iterations include: Let u=1; Determine whether u≤Λ is satisfied; When u≤Λ, the uth effective active antenna combination among the first Λ effective active antenna combinations is taken as the current receiving antenna combination; Calculate the phase vector of the smart reflective surface corresponding to the current receiving antenna combination; Based on the current receiving antenna combination, the superimposed signal of the corresponding row is selected from the received signal matrix, and the superimposed signal is ZF equalized and LoRa incoherently demodulated using the intelligent reflective surface phase vector to obtain the strongest node signal. Let v = 2; Determine if v≤N k If so, then the strongest node signal is used for interference cancellation, and the solved second strongest node signal is subjected to ZF equalization and LoRa incoherent demodulation. After further setting v = v + 1, the process returns to the judgment of whether v ≤ N. k Steps; If not, then calculate the F-norm of the difference between the received signal and the predicted signal; the predicted signal is: the channel matrix, the smart reflector phase vector corresponding to the current receiving antenna combination, and the estimated N. k The product of the superimposed signal vectors reconstructed from the LoRa modulated symbols of each node; Determine whether the F-norm of the difference between the received signal and the predicted signal is less than a preset threshold; if so, update the F-norm of the difference between the received signal and the predicted signal, the index symbol estimate of the active antenna combination obtained in the previous round, and the LoRa symbol estimate, and further set u = u + 1, and return to the step of determining whether u ≤ Λ is satisfied; When u>Λ, output the index symbol estimate and LoRa symbol estimate of the current active antenna combination.

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