Transmitter device and method of operating thereof

Abstract

A transmitter device for employing tensor-based multiple access is disclosed. The transmitter device includes a user group index extractor module which extracts from the input sequence of bits a subsequence of bits corresponding to a user group index. The transmitter device further includes a forward error correction encoder which receives an input of a remaining sequence of bits after the subsequence of bits corresponding to the user group index has been extracted and generates a coded sequence of bits. Further, the transmitter device includes a splitter which receives output of the forward error correction encoder. The transmitter device further includes a plurality of subconstellation vector mapper units and a quasi-orthogonal subconstellation vector mapper unit. The transmitter device further includes a rank one tensor unit which generates a symbols vector by calculating the Kronecker product of the plurality of modulated subconstellation symbol vectors and the quasi- orthogonal subconstellation symbol vector.

Description

[0001]TRANSMITTER DEVICE AND METHOD OF OPERATING THEREOF TECHNICAL FIELD The present disclosure relates generally to the field of wireless communication systems and more specifically, to a transmitterdevice, and a method of operating the transmitter device for employing tensor-based multiple access with quasi-orthogonalconstellation. Moreover, the disclosure relates to a receiving device.BACKGROUND Advancements in the field of wireless communication systems have gained popularity over the years due to their plethora of applications. There is an increasing demand for efficient handling of massive connectivity in the wireless communication systems, especially in the context of the Internet of Things (IoT) and Massive Machine Type Communications (mMTC). The wireless communication systems need robust methods for multiple access to enable a large number of transmitter devices to communicate simultaneously with one or more receivers. Traditional methods often involve complex synchronization processes and are not always scalable enough to handle the vast number of devices expected in future networks. In traditional wireless communication systems, coherent modulation schemes are commonly used, requiring the receiver to synchronize with the phase of the transmitted signal. This synchronization process can be complex and resource-intensive,particularly in environments with high mobility or interference. Existing solutions for multi-user separation, such as OrthogonalFrequency Division Multiple Access (OFDMA) and Code Division Multiple Access (CDMA), also face limitations in scalability and complexity. The devices and methods typically require stringent synchronization and coordination among users,which can be challenging in dynamic and densely populated network scenarios. One of the multiple access schemes is thetensor-based approach. By performing tensor decomposition, the receiver can separate users with high probability as long asthe number of active transmitting users does not exceed a specific upper limit. The limit is associated with the tensor decomposition identification problem, known as the generic rank. The generic rank defines the maximum number of users that can be effectively separated and decoded, ensuring reliable communication even in densely populated networks. The genericrank hinders the number of users in the wireless communication systems.Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated withthe conventional devices and methods for tensor-based multiple access in wireless communication systems.SUMMARYThe present disclosure provides a transmitter device and a method of operating the transmitter for tensor-based multiple access.The present disclosure further provides a receiving device. The present disclosure provides a solution to the existing problemof how to enhance or to increase the limit of users to support more users during tensor-based multiple access. An objective ofthe present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art andprovides improved devices and a method for tensor based multiple access.One or more objectives of the present disclosure are achieved by the solutions provided in the enclosed independent claims.Advantageous implementations of the present disclosure are further defined in the dependent claims.In one aspect, the present disclosure provides a transmitter device. The transmitter device includes a user group index extractormodule which receives an input sequence of bits and extracts from the input sequence of bits a subsequence of bitscorresponding to a user group index. Further, the transmitter device includes a forward error correction encoder which receivesan input of a remaining sequence of bits after the subsequence of bits corresponding to the user group index has been extracted and generates a coded sequence of bits from the remaining sequence of bits. The method further includes a splitter which receives as an input a coded sequence of bits which is output of the forward error correction encoder, and which generates a plurality of subsequences of coded bits by splitting the input coded sequence of bits. The transmitter device further includes a plurality of subconstellation vector mapper units. Each one of the plurality of subconstellation vector mapper receives a respective one of the plurality of subsequences of coded bits output from the splitter and modulates the respective one of theplurality of subsequences of coded bits to generate a plurality of respective subconstellation symbol vectors. A quasi-orthogonalsubconstellation vector mapper unit which receives the subsequence of bits corresponding to the user group index and modulates the received subsequence to generate a quasi-orthogonal subconstellation symbol vector. Furthermore, the transmitter device includes a rank one tensor unit which generates a symbols vector by calculating the Kronecker product of the plurality of modulated subconstellation symbol vectors and the quasi-orthogonal subconstellation symbol vector. The quasi-orthogonal subconstellation vector mapper unit uses sequences such as Galois sequences, Hadamard sequences, Zadoff-Chu sequences, and other advanced sequence types, all designed for quasi-orthogonality. Quasi-orthogonal sequences have low cross-correlation properties, which result in better signal separation and reduced interference among multiple users. The reduced interference leads to clearer and more distinct transmissions, particularly beneficial in dense multi-user environments where it minimizes signal collision and interference. By modulating the user group index using quasi-orthogonal subconstellation vectors, the transmitter device ensures that each user group is uniquely and robustly represented. The uniquerepresentation enhances the integrity and reliability of data transmission, as the quasi-orthogonal properties make the signalsless prone to interference and degradation, even in noisy and dynamic environments. This capability is critical for applications in Massive Machine Type Communications (mMTC) and the Internet of Things (IoT), where reliable data transmission is essential. The quasi-orthogonal subconstellation vector mapper unit allows for the simultaneous transmission of multiple user group signals, increasing the capacity of the communication system to handle a larger number of users concurrently. In environments such as Non-Orthogonal Multiple Access (NOMA), where multiple transmitters operate at the same time, thissignificantly boosts network efficiency and throughput. Additionally, as a wide range of sequence types is used, includingcanonical basis vectors, random Gaussian sequences, Grassmannian sequences, and more. The flexibility allows the transmitterdevice to adapt to different communication requirements and environments. By selecting the most appropriate sequence type,the transmitter device can optimize performance for specific scenarios, whether it be high interference environments, lowlatency requirements, or high data rate needs. The use of quasi-orthogonal sequences simplifies the computation of theKronecker product in the rank one tensor unit, reducing computational complexity. The reduced complexity translates to fasterprocessing times and lower power consumption, which are critical factors in real-time communication systems and battery-operated IoT devices. Further as, messages are transmitted in a random-access or grant-free fashion, which ensures that thetransmitter device handles spontaneous and uncoordinated transmissions without compromising data integrity or increasinglatency. In an implementation form, the plurality of subconstellation vector mapping units are non-coherent modulation units.Advantageously, non-coherent modulation units simplify receiver design by eliminating the need for phase tracking andcompensation, thus removing complex phase-locked loops. The removal of complexity results in a more straightforward, cost-effective, and power-efficient receiver. The non-coherent modulation units enhance robustness, providing better resilience tochannel impairments such as phase shifts and Doppler effects, ensuring reliable performance in dynamic environments like vehicular communication and indoor settings. In an implementation form, the quasi-orthogonal subconstellation vector mapping unit comprises at least the vectors of the canonical basis and vectors from one of Galois sequences, Hadamard sequences, random Gaussian sequences, Grassmannian sequences, Zadoff-Chu sequences, standard legacy preamble sequences, demodulation reference signal, DMRS, sequences, sparse orthogonal vectors, and arbitrary permutations and rotations of any of the previous sequences.Incorporating various sequences enables adaptation to varying channel conditions. Further, support for standard legacypreamble sequences and DMRS ensures seamless integration with existing protocols, facilitating deployment without extensive modifications. Arbitrary permutations and rotations of the sequences increase the number of distinguishable symbols andsimplify detection, enhancing the transmitter device's efficiency and throughput.In an implementation form, the transmitter device is adapted for use in an environment where a plurality of transmitting devices transmits messages in a random-access manner, where a random number of transmitters are active at a given time.Advantageously, the transmitter device is able to efficiently handle accommodating varying traffic loads and eliminates theneed for prior coordination, enabling faster message transmission. Further, the adaptation by the transmitter device decreases complexity in scheduling and synchronization, easing network operations. Further, the transmitter device enhances performance in dynamic and unpredictable environments, such as the IoT.In an implementation form, the transmitter device is adapted for use in an environment where users transmit messages in agrant free fashion, where transmitters transmit messages to a receiver without any prior request. In such an implementation, the immediate message transmission reduces latency, crucial for real-time applications, andsimplifies communication protocols by eliminating the request-grant handshake, enhancing system efficiency. Messagetransmission in the grant-free fashion supports simultaneous transmissions from multiple users, increasing flexibility andadaptability. It improves reliability in dynamic environments, like IoT networks and emergency communications, by ensuring instant message sending. Additionally, message transmission in the grant free fashion reduces the computational and operational burden on transmitter devices, making the wireless communication system more cost-effective and scalable for growing numbers of users without significant infrastructure changes.In an implementation form, the transmitter device is adapted for use in the mMTC environment.Advantageously, the transmitter device for mMTC environments optimizes resource allocation, including bandwidth and power, to maximize network capacity and throughput while conserving resources. The efficiency minimizes power consumption during transmissions, which is crucial for extending device battery life in mMTC scenarios with limited power sources. Advanced modulation and coding techniques enhance signal coverage and penetration, ensuring reliable communication in challenging environments such as indoors or areas with obstacles. This capability supports diverse mMTC applications across wide areas, improving overall connectivity reliability. Compliance with Narrowband IoT (NB-IoT) standards enables seamless integration into global cellular networks, ensuring interoperability and compatibility with existing mMTC deployments.In an implementation form, the transmitter device is adapted for use in an mMTC environment which is an IoT environment.Advantageously, the IoT environment optimizes resource allocation, including bandwidth and power, to efficiently manage and support a large number of interconnected devices. This maximizes network capacity and throughput while conserving scarce resources, which is crucial in IoT deployments where numerous devices need to communicate simultaneously. Furthermore, the transmitter device prioritizes energy efficiency, minimizing power consumption during transmissions. Thepower consumption feature extends the operational lifespan of the IoT devices, particularly those with limited power sources,thereby enhancing overall system sustainability and reducing maintenance requirements.In an implementation form, the transmitter the device is adapted for use in an environment where a plurality of transmitters issimultaneously transmitting a sequence of bits to a single multi-antenna receiver in a Non-Orthogonal Multiple Access (NOMA) fashion. The NOMA efficiently utilizes spectrum by allowing multiple users to share the same frequency and time resources, maximizing spectral efficiency and system capacity without needing more spectrum. The NOMA enhances receiver capability to decode signals from different transmitters by exploiting power domains, improving throughput compared to traditional methods. The NOMA ensures fair resource allocation based on channel conditions and service requirements, optimizing resource use and network performance. Furthermore, the NOMA seamlessly integrates with existing wireless standards,enhancing spectral efficiency and capacity in modern wireless communication systems.In another aspect, the present disclosure provides a method for operating the transmitter device. The method includes receiving,at a user group index extractor module, an input sequence of bits and extracting from the input sequence of bits a subsequenceof bits corresponding to a user group index. The method further includes receiving at a forward error correction encoder, aninput of a remaining sequence of bits after the subsequence of bits corresponding to the user group index has been extractedand generating a coded sequence of bits from the remaining sequence of bits. Further, the method includes receiving at a splitter,a coded sequence of bits which is output of the forward error correction encoder and generating a plurality of subsequences of coded bits by splitting the input coded sequence of bits. The method further includes receiving at a plurality of subconstellationvector mapper units. Each one of the plurality of subconstellation vector mapper receives a respective one of the plurality ofsubsequences of coded bits output from the splitter and modulating the respective one of the plurality of subsequences of codedbits to generate a plurality of respective subconstellation symbol vectors. Also, the method includes receiving at a quasi-orthogonal subconstellation vector mapper unit, the subsequence of bits corresponding to the user group index and modulatingthe received subsequence to generate a quasi-orthogonal subconstellation symbol vector. Furthermore, the method includes generating, at a rank one tensor unit, a symbols vector by calculating the Kronecker product of the plurality of modulated subconstellation symbol vectors and the quasi-orthogonal subconstellation symbol vector.The method achieves all the advantages and technical effects of the transmitter device of the present disclosure.In another aspect, the present disclosure provides a receiving device. The receiving device is configured to obtain a pluralityof user group-specific signals from a received signal based on a plurality of user group indices, wherein each user group- specific signal is for a user group that is identified by their index of the quasi-orthogonal constellation vector. Further, the receiving device is configured to decompose each user group-specific signal into a plurality of user-specific signals, wherein each user-specific signal is for a respective user in the user group. The receiving device is further configured to demap each user-specific signal to obtain a decoded signal using a plurality of sub-constellation demappers except the quasi-orthogonalsub-constellation. The receiving device is further configured to decode each user-specific signal to obtain a decoded sequenceof bits using a forward error correction decoder. Furthermore, the receiving device is configured to merge the decoded sequence of bits with the user group index to obtain the decoded message. The receiving device efficiently extracts user group-specific signals based on their respective indices from the received signal. This allows for simultaneous reception and processing of multiple signals intended for different user groups, enhancing overallsystem throughput and efficiency. The receiving device decomposes each user group-specific signal into individual user-specific signals, enabling separate processing for each user within the group. The capability of the receiving device ensuresthat each user's data is correctly identified and decoded, maintaining data integrity and reliability. Furthermore, the receivingdevice utilizes a variety of sub-constellation demappers, excluding the quasi-orthogonal sub-constellation, to demap user-specific signals and decode them using forward error correction techniques. The decoding approach enhances robustnessagainst errors and noise in the transmission, improving the accuracy of decoded data. By merging the decoded sequence of bitswith the user group index, the receiving device reconstructs the original message. The integration simplifies the decodingprocess and ensures accurate message retrieval, facilitating seamless communication in challenging environments. The designof receiving device supports integration with diverse communication protocols and standards, making it adaptable to variousnetwork configurations and deployments. It is to be appreciated that all the aforementioned implementation forms can be combined.It has to be noted that all devices, elements, circuitry, units, and means described in the present application could beimplemented in the software or hardware elements or any kind of combination thereof. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity which performs that specificstep or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented inrespective software or hardware elements, or any kind of combination thereof. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings andthe detailed description of the illustrative implementations construed in conjunction with the appended claims that follow. BRIEF DESCRIPTION OF THE DRAWINGS The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers. Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:FIG. 1 is a block diagram that depicts a transmitter device for tensor-based multiple access in wireless communication systems,in accordance with an embodiment of the present disclosure;FIG. 2 is a flowchart depicting a method for operating the transmitter device, in accordance with an embodiment of the presentdisclosure; FIG. 3 is an exemplary diagram depicting the execution of the transmitter device, in accordance with an embodiment of the present disclosure; andFIG. 4 is a block diagram that depicts a receiving device, in accordance with an embodiment of present disclosure.In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing. DETAILED DESCRIPTION OF EMBODIMENTS The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.FIG. 1 is a block diagram that depicts a transmitter device for tensor-based multiple access in wireless communication systems,in accordance with an embodiment of the present disclosure, in accordance with an embodiment of the present disclosure. Withreference to FIG.1, there is shown a block diagram 100 that includes a transmitter device 102. The transmitter device 102includes a user group index extractor module 106, a forward error correction (FEC) encoder 108, a splitter 110, a plurality ofsubconstellation vector mapper (SVM) units 112A-N (for example, a first SVM unit 112A, a second SVM unit 112B, up to annth SVM unit 112N), a quasi-orthogonal SVM unit 114 and a rank one tensor unit 116.The transmitter device 102 includes the user group index extractor module 106 which receives an input sequence of bits 104from the input sequence of bits a subsequence of bits corresponding to a user group index. The user group index extractormodule 106 is a component of the wireless communication system that processes and transmits data by extracting a user groupindex, encoding the remaining data with error correction, splitting the encoded data into subsequences, modulating these subsequences into subconstellation symbol vectors, and combining them into a final symbols vector for transmission. The user group index is a specific piece of information encoded in a subsequence of bits within the overall input sequence of bits to betransmitted. The user group index identifies which user group a particular transmitter belongs to. The user group index is usedto select a specific vector from the quasi-orthogonal subconstellation for modulation. The user group index allows transmittersto be grouped and differentiated at the receiver side. The input sequence of bits 104 specifically refers to a stream of binarydigits (i.e., 0s and 1s) that are supplied as data to the transmitter device 102. The input sequence of bits 104 serves as the initialdata source that undergoes further processing and extraction by various modules within the transmitter device 102, such as theuser group index extractor module 106. Advantageously, the user group index extractor module 106 enhances resourceallocation by extracting a specific subset of bits that identifies user groups. The targeted approach reduces unnecessarybroadcast and minimizes interference with unrelated transmissions, thereby lowering network overhead. It improves spectral efficiency, leading to reduced energy consumption. Additionally, by organizing communication channels based on user groupindices, the module optimizes network capacity. This enables the transmitter device 102 to support more users and devicessimultaneously within the same bandwidth allocation, facilitating efficient and concurrent data transmissions.In operation, the user group index extractor module 106 starts by scanning the incoming input sequence of bits 104 to identifythe location and structure of the subsequence that contains the user group index. The subsequence is a predefined segment ofbits within the larger data stream. Within the input sequence of bits 104, there may be specific patterns, markers, or headersthat indicate the beginning and end of the user group index subsequence. These patterns serve as cues for the user group indexextractor module 106 to accurately pinpoint where the relevant bits are located. Once the user group index extractor module106 identifies the starting point and structure of the user group index subsequence, it proceeds to segment and extract thisportion from the input sequence of bits 104. The segmentation process ensures that only the bits corresponding to the usergroup index are selected while discarding irrelevant data. For example, we have an input sequence of 20 bits, and the first 4bits represent the user group index. If the input sequence is “1011 0110 1010 1100 1001.” The user group index extractormodule 106 receives the entire twenty-bit sequence. The user group index extractor module 106 extracts the first 4 bits for the user group index. The user group index extractormodule 106 extracts “1011” as the user group index. The user group index extractor module 106 separates “1011” from theremaining sixteen bits. The user group index extractor module 106 outputs the user group index bits “1011.” The “1011” issent to the quasi-orthogonal SVM unit 114, while the remaining sixteen bits are sent to the FEC encoder 108 for furtherprocessing. The separation allows the transmitter device 102 to handle the user group identification separately from the maindata, enabling the use of the quasi-orthogonal subconstellation for improved user separation in the multiple access scheme.In accordance with an embodiment, the transmitter device 102 is adapted for use in an environment where a plurality oftransmitting devices transmits messages in a random-access manner, where a random number of transmitters are active at agiven time. The transmitter device 102 can handle a varying number of active users without prior scheduling or resourceallocation. This is crucial in IoT scenarios where the number of active devices can fluctuate dramatically. Further, it eliminatesthe need for complex scheduling and resource allocation protocols and reduces signalling overhead, as the transmitter devicescan transmit when they have data without requesting permission. The transmitter device 102 can remain in low-power modeand only activate when it needs to transmit, which reduces energy consumption, which is crucial for battery-operated IoTdevices. The tensor-based approach allows for efficient use of spectral resources even with multiple simultaneous transmissions and can potentially support a higher number of devices in a given bandwidth compared to traditional scheduled access. Thetransmitter device 102 can handle diverse types of devices with different transmission patterns and requirements.In accordance with an embodiment, the transmitter device 102 is adapted for use in an environment where users transmitmessages in a grant free fashion, where transmitters transmit messages to a receiver without any prior request. The transmitterdevice 102 is optimized for grant-free environments, allowing immediate message transmission without prior requests. Theimmediate message transmission reduces latency, which is crucial for real-time applications and simplifies communication protocols by eliminating the request-grant handshake, enhancing system efficiency. Grant-free operation supports simultaneous transmissions from multiple users, increasing flexibility and adaptability. It improves reliability in dynamic environments, like IoT networks and emergency communications, by ensuring instant message sending. Additionally, it reduces the computational and operational burden on devices, making the system more cost-effective and scalable for growing numbers of users without significant infrastructure changes.The transmitter device 102 further includes the FEC encoder 108 which receives an input of a remaining sequence of bits afterthe subsequence of bits corresponding to the user group index has been extracted and generates a coded sequence of bits from the remaining sequence of bits. The FEC encoder 108 is a digital signal processing component used in wireless communication systems to enhance the reliability of data transmission. It operates by adding redundant information (error-correcting codes) to the original data bits before transmission. This redundancy allows the receiver to detect and correct errors that may occur during data transmission, thereby improving the overall accuracy and integrity of the received data.In operation, the FEC encoder 108 receives the remaining sequence of bits after the user group index bits have been extracted.Sixteen bits, i.e.,”0110 1010 1100 1001”. The main purpose of the FEC encoder 108 is to add redundancy to the data in astructured way. The redundancy allows the receiver to detect and correct errors that might occur during transmission. The FECencoder 108 applies a specific error correction algorithm to the input bits. The FEC encoder 108 takes the input bits andgenerates additional parity bits based on the chosen algorithm (such as Convolutional codes, Turbo codes, Low-density parity-check (LDPC) codes, and Reed-Solomon codes). These parity bits are calculated from the input data bits according to specificmathematical rules. The FEC encoder 108 outputs a new sequence of bits that includes both the original data bits and the newlygenerated parity bits. The new sequence is longer than the input sequence due to the added redundancy. The ratio of input bitsto output bits is called the coding rate. For example, if the encoder takes 16 bits and outputs 24 bits, the coding rate would be16 / 24 = 2 / 3. The FEC encoder 108 uses a simple rate 1 / 2 convolutional code. This means for every input bit; it produces twooutput bits. The Input to FEC encoder 108 is 0110101011001001 (sixteen bits). The FEC encoder 108 processes these bitsand produces output, i.e., 00111100100111001110000010001101 (thirty-two bits). In this output, the original sixteenbits are still present (though possibly rearranged). An additional sixteen bits have been added as parity information. The codedsequence of bits is then passed on to the next component in the splitter 110, carrying both the original data and the errorcorrection information.Advantageously, by adding redundancy to the data, the FEC encoder 108 enhances the receiver's ability to detect and correcttransmission errors, ensuring data integrity even in noisy or interference-prone environments. This improves overall reliability by minimizing data corruption and reducing the necessity for retransmissions, thus optimizing transmission efficiency. Despiteincreasing the sequence length compared to the original data, FEC encoder 108 efficiently utilizes available bandwidth. Itminimizes the overhead typically needed for error recovery in the transmitter devices lacking the FEC encoder 108, maximizingeffective data throughput.The transmitter device 102 further includes the splitter 110 which receives as an input a coded sequence of bits which is outputof the FEC encoder 108, and which generates a plurality of subsequences of coded bits by splitting the input coded sequenceof bits. The splitter 110 is a component within transmitter device 102 that receives a single input sequence of data or signal anddivides it into multiple smaller segments or channels. The division allows for the distribution of the input data or signal todifferent processing units or transmission paths, enabling parallel or distributed processing, transmission, or further modulationof the data. In operation, the splitter 110 receives the coded sequence of bits from the FEC encoder 108. The splitter 110 dividesthe coded sequence into a predetermined number of subsequences. The number of subsequences typically corresponds to thenumber of subconstellation vector mapper units in the transmitter device 102. The splitter 110 outputs these subsequences,each of which will be sent to a different subconstellation vector mapper unit. For example, in case of an even split, if inputfrom FEC encoder 108 is 110010110101001100 (eighteen bits), the splitter 110 generates three number of subsequences, i.e.,a first subsequence is 110010 (six bits), a second subsequence is 110101 (six bits) and a third subsequence is 001100 (six bits).Further, in case of uneven split, if input from the FEC encoder 108 is 1100101101010011001101 (twenty bits) and the FECencoder 108 generates four subsequences i.e., a first subsequence which is 110010 (six bits), a second subsequence which is110101 (six bits), a third subsequence which is 001100 (six bits), a fourth subsequence which is 1101 (four bits). In each case,the splitter 110 ensures that the entire coded sequence is divided into the required number of subsequences. If the division isnot even, it might use padding or have some subsequences shorter than others, depending on the specific implementation. Thesubsequences are then passed on to the individual subconstellation vector mapper units for further processing. The splittingallows the system to map various parts of the message onto different dimensions in the final tensor product, which is key to thetensor-based modulation scheme used in the transmitter device 102.By splitting the coded sequence of the FEC encoder 108 into multiple subsequences, the splitter 110 enhances the efficiencyof the transmitter device 102 with parallel processing, boosting throughput and reducing latency. The splitter 110 optimizesresource use by distributing workload across subsequences, ensuring efficient allocation of processing power and bandwidthwithout overload. The ability of the splitter 110 to generate multiple subsequences supports flexible transmission strategiestailored to diverse communication needs and improves fault tolerance by enabling uninterrupted data delivery despite errors orinterference, enhancing overall reliability.The transmitter device 102 further includes the plurality of SVM units 112A-N wherein each one of the plurality ofsubconstellation vector mapper receives a respective one of the plurality of subsequences of coded bits output from the splitter 110 and modulates the respective one of the plurality of subsequences of coded bits to generate a plurality of respectivesubconstellation symbol vectors. The plurality of SCM units 112 refers to multiple (two or more) mapper units in the system.Each unit operates independently but in parallel with the others. Each SCM unit receives one subsequence of coded bits fromthe splitter 110. These subsequences are parts of the larger coded sequence that was split earlier.For example, in QPSK (Quadrature Phase Shift Keying), bits are grouped in pairs; in 16- quadrature amplitude modulation(QAM), they are grouped in sets of four. Each group of bits is mapped to a complex symbol according to the modulationconstellation. In an example, in QPSK: “00” map to “1+1i”, “01” map to “1-1i”, “10 map to “-1+1i”, “11” to “-1-1i”.Further the mapped symbols are arranged into a vector. Each SVM unit of the plurality of SVM units 112 produces asubconstellation symbol vector. This vector contains the complex symbols that represent the input bits. The outputs from theplurality of SVM units 112 together form the "plurality of respective subconstellation symbol vectors".In accordance with an embodiment, the plurality of subconstellation vector mapping units are non-coherent modulation units. Non-coherent modulation simplifies receiver design by eliminating the need for phase synchronization, reducing complexity and cost. It enhances robustness by being less sensitive to phase noise and Doppler shifts, improving performance in high- interference and high-mobility environments. Energy efficiency is achieved through lower power consumption and extended battery life, which is vital for IoT and mMTC devices. The technology supports massive connectivity and grant-free access, which is essential for scalable network deployments.The transmitter device 102 further includes the quasi-orthogonal SVM unit 114, which receives the subsequence of bitscorresponding to the user group index and modulates the received subsequence to generate a quasi-orthogonal subconstellationsymbol vector. In operation, the quasi-orthogonal SVM unit 114 receives the subsequence of bits corresponding to the usergroup index. The subsequence earlier extracted by the user group index extractor module 106.For example, the quasi-orthogonal SVM unit 114 receives a 4-bit sequence like "1011". The quasi-orthogonal SVM unit 114uses a predefined quasi-orthogonal subconstellation. This subconstellation typically consists of a set of orthogonal vectors(often the canonical basis vectors) and additional non-orthogonal vectors.For example, in a 4-dimensional space, the quasi-orthogonal constellations are:[1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1] (orthogonal vectors) [0.5,0.5,0.5,0.5] (non-orthogonal vector)The input bit sequence is used to select a vector from the quasi-orthogonal subconstellation. The mapping is predetermined andknown to both the transmitter and receiver. For example:"0000" map to [1,0,0,0], "0001" map to [0,1,0,0], and in a comparable manner, "1111" may map to [0.5,0.5,0.5,0.5].The quasi-orthogonal SVM unit 114 "modulates" the input by performing this mapping. It is not modulation in the traditionalsense (like QAM) but rather a selection of a vector based on the input bits. The selected vector becomes the quasi-orthogonalsubconstellation symbol vector. This vector represents the user group index in a form that can be used in the tensor-based transmission scheme.The quasi-orthogonal SVM unit 114 allows for improved user separation at the receiver. The quasi-orthogonal nature of thesubconstellation helps in distinguishing between different user groups, even when multiple users are transmittingsimultaneously. The approach can potentially increase the number of users that can be simultaneously served in a non-orthogonal multiple access system. The output of the quasi-orthogonal subconstellation vector mapper unit will later becombined with the outputs from the plurality of subconstellation vector mapper units 112A-N using the Kronecker product inthe rank one tensor unit 116. The combination creates the final symbols vector for transmission, embedding the user group information in a way that can be efficiently decoded at the receiver.In accordance with an embodiment, the quasi-orthogonal SVM unit 114 comprises at least the vectors of the canonical basisand vectors from one of Galois sequences, Hadamard sequences, random Gaussian sequences, Grassmannian sequences, Zadoff-Chu sequences, standard legacy preamble sequences, demodulation reference signal, DMRS, sequences, sparse orthogonal vectors, and arbitrary permutations and rotations of any of the previous sequences. Incorporating sequences like Galois, Hadamard, and Zadoff-Chu enhances signal robustness by maintaining orthogonality, ensuring reliable detection in challenging environments. Random Gaussian, Grassmannian sequences, and sparse orthogonal vectors enable adaptation to varying channel conditions. Further, support for standard legacy preamble sequences and DMRS ensures seamless integration with existing protocols, facilitating deployment without extensive modifications. Arbitrary permutations and rotations of the sequences increase the number of distinguishable symbols and simplify detection, enhancingthe transmitter device's efficiency and throughput. Sequences based on Galois fields and sparse orthogonal vectors provideinherent security benefits, reducing interception risks and quality metrics measurement allows comprehensive signal quality assessment.The transmitter device 102 further includes the rank one tensor unit 116, which generates a symbols vector by calculating theKronecker product of the plurality of modulated subconstellation symbol vectors and the quasi-orthogonal subconstellationsymbol vector. In operation, the rank one tensor unit 116 takes the plurality of modulated subconstellation symbol vectors fromthe plurality SVM units 112A-N and the quasi-orthogonal subconstellation symbol vector from the quasi-orthogonal SVM unit114. The rank one tensor unit 116 calculates the Kronecker product of all these vectors. The Kronecker product is a specialoperation that combines vectors or matrices to produce a larger matrix. Kronecker Product:For vectors a = [a1, a2] and b = [b1, b2, b3],the Kronecker product a ⊗ b is: [a1b1, a1b2, a1b3, a2b1, a2b2, a2b3].First, rank one tensor unit 116 calculates the Kronecker product of all the modulated subconstellation symbol vectors. Then,rank one tensor unit 116 calculates the Kronecker product of this result with the quasi-orthogonal subconstellation symbolvector. The final output is a single, higher-dimensional symbols vector.If two modulated subconstellation symbol vectors are “v1” = [1, -1] and “v2” = [i, -i].If a quasi-orthogonal subconstellation symbol vector be “q” = [0.7, 0.7].The Kronecker product of v1 and v2:v1 ⊗ v2 = [1i, 1(-i), -1i, -1(-i)] = [i, -i, -i, i]and Kronecker product of “(v1 ⊗ v2)” and “q” is:(v1 ⊗ v2) ⊗ q = [i0.7, i0.7, -i0.7, -i0.7, -i0.7, -i0.7, i0.7, i0.7]= [0.7i, 0.7i, -0.7i, -0.7i, -0.7i, -0.7i, 0.7i, 0.7i].The final vector is the symbols vector generated by the rank one tensor unit 116. The operation combines the data (representedby the modulated subconstellation symbol vectors) with the user group information (represented by the quasi-orthogonalsubconstellation symbol vector). The resulting symbols vector has a specific structure that allows for efficient multi-userdetection at the receiver. The use of the Kronecker product creates a unique tensor structure that is key to the tensor-basedmultiple access scheme. This approach allows for improved spectral efficiency and user separation in multi-user scenarios. Thesymbols vector produced by this unit is then ready for further processing (e.g., adding cyclic prefix, pulse shaping) before being transmitted over the channel.In accordance with an embodiment, the transmitter device 102 is adapted for use in a Massive Machine Type Communications(mMTC) environment. Advantageously, the transmitter device 102 for mMTC environments optimizes resource allocation,including bandwidth and power, to maximize network capacity and throughput while conserving resources. The efficiency minimizes power consumption during transmissions, crucial for extending device battery life in mMTC scenarios with limited power sources. Advanced modulation and coding techniques enhance signal coverage and penetration, ensuring reliable communication in challenging environments such as indoors or areas with obstacles. This capability supports diverse mMTC applications across wide areas, improving overall connectivity reliability.In accordance with an embodiment, the transmitter device 102 is adapted for use in the mMTC environment which is an Internetof Things (IoT) environment. Advantageously, use in IoT environments maximizes network capacity and throughput while conserving scarce resources, which is crucial in IoT deployments where numerous devices need to communicate simultaneously. Furthermore, the transmitter device prioritizes energy efficiency, minimizing power consumption during transmissions. This feature extends the operational lifespan of IoT devices, particularly those with limited power sources, thereby enhancing overall system sustainability and reducing maintenance requirements.In accordance with an embodiment, the transmitter device 102 is adapted for use in an environment where a plurality oftransmitters is simultaneously transmitting a sequence of bits to a single multi-antenna receiver in a Non-Orthogonal MultipleAccess (NOMA) fashion. The NOMA efficiently utilizes spectrum by allowing multiple users to share the same frequency andtime resources, maximizing spectral efficiency and system capacity without needing more spectrum. The NOMA enhances the receiver capability to decode signals from different transmitters by exploiting power domains, improving throughput compared to traditional methods. The NOMA ensures fair resource allocation based on channel conditions and service requirements, optimizing resource use and network performance. Furthermore, the NOMA seamlessly integrates with existing wireless standards, enhancing spectral efficiency and capacity in modern wireless communication systems.FIG. 2 is a flowchart depicting a method for operating the transmitter device, in accordance with an embodiment of the presentdisclosure. FIG. 2 is described in conjunction with FIG. 1. With reference to FIG. 2, there is shown a flowchart of a method200 for operating the transmitter device 102. The method 200 includes steps 202 to 212.At step 202, the method 200 includes receiving, at the user group index extractor module 106, an input sequence of bits 104and extracting from the input sequence of bits a subsequence of bits corresponding to a user group index. The process startswhen the user group index extractor module 106 receives a stream or batch of bits referred to as the input sequence of bits 104.The sequence represents the data that needs to be processed by the system. Within the input sequence, there is a specific segmentof bits that represents the user group index. The user group index extractor module 106 scans the entire input sequence of bits104 to locate the portion that matches the criteria for the user group index. Once the user group index is identified, the moduleisolates this subsequence from the rest of the input sequence. The isolation process involves extracting the bits that specificallycorrespond to the user group index. The result of this extraction is a shorter subsequence of bits that is designated as the user group index. This subsequence will be used in subsequent steps to ensure that the data is correctly processed according to its group designation. For example, if the input sequence is “10111010010100111010”. The full sequence“10111010010100111010” is received by the user group index extractor module 106. For example, the user group index isdefined by the bit pattern “0101”. The user group index extractor module 106 scans the input sequence to locate this pattern.The user group index extractor module 106 identifies ":0101” at positions “8” to “11” within the sequence. The identified usergroup index “0101” is isolated from the input sequence. The rest of the sequence, excluding the user group index, is set asidefor further processing. The extracted subsequence “0101” is the user group index, which will be used in subsequent processing steps. Extracting the user group index is crucial for categorizing the data and routing it through the correct processing pathways within the system. It ensures that data meant for specific user groups is handled appropriately. By isolating the user group indexearly in the process, the transmitter device 102 can efficiently manage and process data according to group-specificrequirements, improving overall system performance and accuracy.At step 204, the method 200 includes receiving at the FEC encoder 108, an input of a remaining sequence of bits after thesubsequence of bits corresponding to the user group index has been extracted and generating a coded sequence of bits from theremaining sequence of bits. After the user group index extractor module 106 has isolated and removed the subsequence of bitscorresponding to the user group index from the input sequence, what remains is referred to as the "remaining sequence of bits."For example, the initial input sequence was “10111010010100111010”. The user group index extractor 106 removed “0101”(the user group index). The remaining sequence of bits could be something like “1011101000111010”. The FEC encoder 108applies a specific error correction algorithm to the input bits. This process involves adding redundant bits to the original databits based on mathematical rules defined by the chosen FEC algorithm. The FEC encoder 108 calculates parity bits from theinput data bits. These parity bits are additional bits that help detect and correct errors during data transmission. The input data bits and the calculated parity bits are combined to form a new, longer sequence of bits known as the coded sequence of bits.The output from the FEC encoder 108 is a coded sequence of bits, which includes both the original data bits and the addedparity bits. This sequence is longer than the original remaining sequence due to the redundancy added. The main purpose ofadding redundancy through FEC encoder 108 is to allow the receiver to detect and correct errors that occur during thetransmission. By encoding the data with error correction codes, the system ensures that even if some bits get corrupted during transmission, the original data can still be accurately reconstructed at the receiver end. While the coded sequence is longer, itreduces the need for retransmissions due to errors, thus optimizing the overall efficiency and reliability of the communicationsystem.At step 206, the method 200 includes receiving at the splitter 110, a coded sequence of bits which is output of the FEC encoder108 and generating a plurality of subsequences of coded bits by splitting the input coded sequence of bits. The coded sequenceof bits is the output from the FEC encoder 108. This sequence includes both the original data bits and the additional parity bitsadded by the FEC process. If the remaining sequence of bits was “1011101001111010” and the FEC encoder 108 used a rate1 / 2 convolutional code, the output coded sequence might be 11001100101111001010100101101100. The splitter 110 receivesthis entire coded sequence of bits as its input. The splitter 110 divides the input-coded sequence of bits into multiple smallersegments or subsequences. Each subsequence will then be processed independently in subsequent steps. The splitter 110 dividesthe sequence in various ways, depending on the design and requirements of the transmitter device 102. The output from thesplitter 110 consists of multiple subsequences of coded bits. The subsequences are the smaller segments of the original codedsequence. If the coded sequence is “11001100101111001010100101101100” (32 bits). The splitter 110 divides the codedsequence into four subsequences. By dividing the coded sequence into multiple subsequences, the splitter can process thesesubsequences in parallel. This enhances the overall efficiency and speed of data processing and transmission. Splitting thesequence allows for better utilization of available resources, such as processing power and transmission bandwidth. Eachsubsequence is handled by different components or channels, preventing any single component from becoming a bottleneck. Ifone subsequence encounters errors or interference during transmission, the other subsequences can still be processed correctly.At step 208, the method 200 includes receiving at the plurality of SVM units 112A-N. Each one of the plurality ofsubconstellation vector mapper receives a respective one of the plurality of subsequences of coded bits output from the splitter110 and modulating the respective one of the plurality of subsequences of coded bits to generate a plurality of respectivesubconstellation symbol vectors. Each subconstellation vector mapper unit receives a respective subsequence of coded bits thatwere output by the splitter. Each of these subsequences is fed into a separate SVM unit. The plurality of SVM units 112A-Napply a modulation scheme to convert the subsequences of bits into symbols. Each of SVM unit of the plurality of SVM units112A-N converts the received bits into corresponding symbols based on the chosen modulation scheme.After modulation, each of the mapper unit of the plurality of SVM units 112A-N generates the subconstellation symbol vectorfrom the modulated symbols. The symbol vector is a sequence of modulated symbols that correspond to the input bits. Similarly,other mapper units generate their respective subconstellation symbol vectors. Each mapper unit outputs its respectivesubconstellation symbol vector. These vectors are then used in subsequent processing stages, such as combining with othervectors in a rank one tensor unit.By dividing the workload across multiple mapper units, the transmitter device 102 is able to handle more data simultaneously,increasing overall throughput and reducing latency. The plurality of SVM units 112A-N can use different modulation schemestailored to the specific requirements of their respective subsequences. This approach ensures efficient use of processingresources, as each mapper unit can operate independently and simultaneously.At step 210, the method 200 further includes receiving at the quasi-orthogonal SVM unit 114, the subsequence of bitscorresponding to the user group index and modulating the received subsequence to generate a quasi-orthogonal subconstellationsymbol vector. The quasi-orthogonal SVM unit 114 modulates these bits to generate a quasi-orthogonal subconstellationsymbol vector. This step is crucial for distinguishing data from different user groups in a communication system. The quasi-orthogonal SVM unit 114 receives a subsequence of bits that corresponds to a user group index. The subsequence waspreviously extracted by the user group index extractor module. The modulation process converts the subsequence of bits intoa symbol vector. The quasi-orthogonal modulation schemes are used to ensure that the resulting symbols have properties thatmake them easily distinguishable from symbols generated for other user groups. The quasi-orthogonal SVM unit 114 can usevarious sequences such as Galois sequences, Hadamard sequences, Random Gaussian sequences, Standard legacy preamblesequences, etc. The received subsequence of bits is converted into symbols according to the chosen quasi-orthogonalmodulation scheme. In an example, if the modulation scheme is based on Hadamard sequences, each bit or group of bits in thesubsequence is mapped to a unique symbol in the Hadamard codebook. The resulting symbols are arranged into a symbolvector. This symbol vector represents the modulated form of the user group index subsequence. The quasi-orthogonal SVMunit 114 outputs the quasi-orthogonal subconstellation symbol vector. For the input subsequence “101011”, if using aHadamard sequence, the output might be something like [H1, H0, H1, H0, H1, H0] where H0 and H1 are Hadamard symbols. The quasi-orthogonal subconstellation symbol vector helps in differentiating between user groups, allowing the system tohandle multiple users simultaneously without confusion. By using quasi-orthogonal symbol vectors, the transmitter device 102balances the interference between different users' data, ensuring reliable communication even in noisy environments. Themodulation technique enables the system to use available bandwidth and processing power more efficiently, supporting a larger number of users.Initial Subsequence Input “101011”. The Hadamard sequence maps bits as follows:0 → Symbol H0 1 → Symbol H1 Mapping Bits to Symbols: 1 → H1 0 → H0 1 → H1 0 → H0 1 → H1 1 → H1The generated symbol vector is [H1, H0, H1, H0, H1, H1] which is quasi-orthogonal subconstellation symbol vector.At step 212, the method 200 further includes generating, at the rank one tensor unit 116, the symbols vector by calculating theKronecker product of the plurality of modulated subconstellation symbol vectors and the quasi-orthogonal subconstellationsymbol vector. The modulated subconstellation symbol vectors are generated by the SVM units, i.e. the plurality of SVM units112A-N. Each SVM unit modulates a subsequence of coded bits, resulting in a set of symbol vectors. The quasi-orthogonalsubconstellation symbol vector is generated by and the quasi-orthogonal SVM unit 114, which processes the user group indexbits. The Kronecker product is a mathematical operation that takes two matrices (or vectors) and produces a block matrix. Forvectors, it produces a larger vector by multiplying each element of the first vector with the entire second vector. The rank onetensor unit 116 receives the modulated subconstellation symbol vectors and the quasi-orthogonal subconstellation symbolvector. For each element in the quasi-orthogonal subconstellation symbol vector, the unit multiplies this element by the entiremodulated subconstellation symbol vector. The results of these multiplications are concatenated to form a single, larger symbolsvector. The symbol vector captures the combined information of the original vectors in a structured way.The steps 202 to 212 are only illustrative, and other alternatives can also be provided where one or more steps are added, oneor more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of theclaims herein. FIG. 3 is an exemplary diagram depicting the execution of the transmitter device, in accordance with an embodiment of thepresent disclosure FIG. 3 is described in conjunction with elements from FIG. 1 and 2. With reference to FIG. 3, there is shownan exemplary diagram 300 that depicts tensor-based signalling architecture in transmitter device 102. The exemplary diagram300 includes a series of operations from 302 to 312. The transmitter device 102 (of FIG. 1) is configured to execute theoperations shown in the exemplary diagram 300.At operation 302, a payload (the initial sequence of B bits for the user) for a kth user is fed into the FEC encoder 108. Thepayload goes through the FEC encoder 108. The FEC encoder 108 outputs a sequence of coded bits denoted by “B”̃ (where B̃> B due to added redundancy).At operation 304, the coded bits “B”̃ are split into “d” subsequences. Each subsequence has size Bi, where B1 + B2 + ... + Bd= B.̃ There are d sub-constellations in total (D1, D2, ..., Dd). The first d-1 sub-constellations (D1, D2..., Dd-1) are standardvector constellations (e.g., QAM). The last sub-constellation (Dd) is the special quasi-orthogonal sub-constellation.At operations 306 and 308, each of the plurality sub-constellation units 112A-N takes a subsequence of bits and outputs acorresponding vector of symbols ^^,^ …, ^^^^,^.At operation 310, the quasi-orthogonal sub-constellation unit 114 generates ^^,^.The designed encoder where the last sub-constellation is a quasi-orthogonal sub-constellation containing ^^ + ^ vectors,denoted by Td + p vectors. The first Td vectors form the canonical orthogonal basis. The remaining p vectors are non-orthogonalto at least one canonical basis vector.The quasi-orthogonal sub-constellation containing ^^ + ^ vectors, denoted by At operation 312, the rank one tensor unit 116 takes all the symbol vectors, i. e., ^^,^ , … , ^^,^ as input and computes theKronecker product of these symbol vectors. Finally, the rank one tensor unit 116 outputs the final transmitted signal sk for userk. The received signal with respect to the described tensor-based scheme where one of the sub-constellations has a particular design as a quasi-orthogonal sub-constellation is given by “equation one” as follows: where: -⊗ denotes the Kronecker product,- ^^ = ^^^ ⊗ … ⊗ ^^^ ∈ ℂ^ is the transmitted signal (rank-one tensor) of the user ^ (ℂ denotes the set of complexnumbers, and its superscript is the dimension of the related vector / matrix),- ^^ ∈ ℂ^is the channel gain, -^ ∈ ℂ^ is the noise.In the “equation one”, the transmitting users are grouped in L groups, denoted by ^^, … , ^^ depending on the used vector inthe quasi-orthogonal sub-constellation: each user in the group ^^used the vector ^^^^in the sub-constellation ^= ^^ at thetransmitter device 102 side. The decoder then alternates between successive independent group decoding and joint groupdecoding.In an implementation, the “non-orthogonal” vectors ^^^ ^^, ... , ^^^^^ can be generated amongst any of the following family ofvectors such as Grassmannian sequences, columns of Fourier matrix, Zadoff-Chu vectors, columns of Hadamard matrix, afamily of sparse orthogonal vectors (that is, the vectors of the family contain few nonzero elements).An important example of a sparse orthogonal vector family is as follows: Any arbitrary rotation of the following sequences can be considered.For each choice of the “non-orthogonal vectors,” ^^^ ^^ , ... , ^^^ ^^, a rescaled version ^^^^^^ , ... , ^^^^^^ with -with ^^ = ^(1 / ^)^^^ ^^ guaranteeing an interference-to-signal ratio parameterized by ^ at the receiver when^^^^^ , ... , ^^^^^ are orthogonal to each other.For p = 1 “design one” is obtained:Design one: For some parameter ^, the vector such that the first elements are 1, the last are zeros. There is provided a computer program comprising instructions that, when executed by a computer system, cause the computer system to implement the method 200. In an example, the instructions are implemented on the computer-readable media, which include, but are not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), a computer-readable storage medium, and / or CPU cache memory.FIG. 4 is a block diagram that depicts a receiving device, in accordance with an embodiment of present disclosure. Withreference to FIG. 4, there is shown a block diagram 400 which includes a receiving device 402. The receiver device 402 includesa specific signal extractor 406, a signal decomposer 408, a plurality of sub-constellation demappers 410, a FEC decoder 412and a merger 414.The receiving device 402 is a wireless communication component designed to intercept, process, and decode signals transmittedby multiple user groups in environments such as IoT, mMTC, and NOMA. The receiving device 402 is configured to managesimultaneous transmissions efficiently and ensure reliable data extraction by utilizing advanced signal processing techniques and forward error correction.The specific signal extractor 406 within the receiving device 402 designed to isolate and extract user group-specific signalsfrom a received composite signal. The process of extraction is essential in environments where multiple user groups transmit data simultaneously, ensuring that each group's signal can be individually identified and processed.The signal decomposer 408 is in the receiving device 402 and is responsible for breaking down user group-specific signals intoindividual user-specific signals. By employing advanced signal processing techniques, the signal decomposer ensures accurate isolation and recovery of each user's transmitted data, enhancing the overall performance and reliability of the communication system in multi-user environments.The plurality of sub-constellation demappers 410 is in receiving device 402 is responsible for converting modulated user-specific signals back into their original bit sequences. By employing multiple demappers tailored to different sub-constellations,the receiver device 402 ensures accurate and efficient data recovery.The FEC decoder 412 refers to the unit within the receiving device 402, which corrects errors in the received and demappedbit sequences to ensure data integrity and reliability and enhances robustness of data transmission in the wirelesscommunications, especially in environments prone to noise and interference.There is provided the receiving device 402, which is configured to obtain a plurality of user group-specific signals from areceived signal based on a plurality of user group indices, wherein each user group-specific signal is for a user group that isidentified by their index of the quasi-orthogonal constellation vector. The receiving device 402 captures a composite signal,which is a mix of transmissions from multiple user groups. In multi-user environments, such as IoT or mMTC, many devices or user groups transmit data simultaneously, leading to a composite signal that needs to be separated for further processing.Each user group is identified by a unique index. The unique index is embedded in the transmitted signal using quasi-orthogonalconstellation vectors. These indices allow the receiver device to distinguish between different user groups within the compositesignal. The receiving device 402 uses correlation or matched filtering techniques to identify and isolate the segments of thecomposite signal that correspond to each user group index. The quasi-orthogonal constellation vectors are special vectors usedto encode the user group indices in a way that minimizes interference between signals. The quasi-orthogonal constellationvectors have low cross-correlation properties, which means they can be effectively distinguished from each other, even whenmultiple signals are transmitted simultaneously. The receiving device 402 performs mathematical operations to match thesevectors with parts of the received signal, effectively isolating signals corresponding to different user groups. Based on theidentified indices, the receiving device 402 extracts specific portions of the composite signal that correspond to each user group.This ensures that the data for each user group is separated and can be processed individually, preventing interference andcrosstalk. The receiving device 402 uses signal processing techniques such as filtering and correlation analysis to isolate andextract the user group-specific signals. A technique where the received signal is passed through a filter that is matched to the expected quasi-orthogonal constellation vector. This maximizes the signal-to-noise ratio for the desired signal and helps in identifying the user group-specific signals. The correlation analysis involves computing the correlation between the received signal 404 and the quasi-orthogonal constellation vectors. High correlation values indicate the presence of a specific user group’s signal, enabling its extraction. In IoT environments, numerous devices (sensors, actuators, etc.) transmit data to acentral receiver. The receiving device 402 can efficiently separate and process the signals from different user groups (e.g.,temperature sensors, motion detectors) based on their unique indices. In mMTC scenarios, a large number of devices transmitdata simultaneously. The receiving device 402 ensures that signals from different machine-type devices are accurately isolatedand processed. In NOMA systems, multiple users share the same transmission resources. The receiving device 402 can separateand process the signals from different users by using their unique quasi-orthogonal indices.The receiver device 402 is further configured to decompose each user group-specific signal into a plurality of user-specificsignals, wherein each user-specific signal is for a respective user in the user group. The process involves breaking down a usergroup-specific signal using the signal decomposer 408, which contains combined data from multiple users within a group, into individual signals corresponding to each user. This ensures that each user's data is isolated for accurate decoding and further processing. To ensure that the data for each user is accurately extracted without interference from other users in the same group. To allow for individual processing of each user’s data, facilitating error correction, decoding, and further communication tasks.To reduce crosstalk and interference between signals from different users within the same group. The receiving device 402captures a signal that contains data from multiple users within a group. The signal is received through antennas and processedto identify the group-specific signal. Further identifies unique characteristics or identifiers for each user’s signal within thegroup-specific signal. Each user's signal has distinct features that can be used to differentiate it from others. The signaldecomposer 408 applies filters to target specific frequency ranges or signal characteristics unique to each user. Different usersmay transmit in slightly different frequency bands or have unique signal patterns. Use bandpass filters, matched filters, oradaptive filters to isolate each user’s signal. The signal decomposer 408 decomposes the signal matrix into individualcomponents using techniques like Singular Value Decomposition (SVD) or Independent Component Analysis (ICA). Matrixdecomposition helps in separating mixed signals by breaking down the composite signal into its underlying components. Thedecomposition process results in multiple user-specific signals, each corresponding to a different user within the group. Toensure that each user’s data is isolated and can be individually processed without interference. The isolated signals are thenforwarded to subsequent processing stages, such as demapping and error correction.The receiver device 402 is further configured to demap each user-specific signal to obtain a decoded signal using a plurality ofsub-constellation demappers except the quasi-orthogonal sub-constellation. The main objective is to decode each user-specificsignal to retrieve the original data bits, ensuring accurate data reconstruction after transmission and reception.The plurality of sub-constellation demappers 410 receives individual user-specific signals that have been decomposed andisolated from the composite signal. These signals contain modulated data that needs to be converted back into its original bitformat for further processing and interpretation. The plurality of sub-constellation demappers reverses the modulation processapplied during signal transmission. The reversal is done to accurately recover the original bit sequences from the modulatedsignals, allowing for error correction and subsequent data processing. The plurality of sub-constellation demappers 410 appliesdemapping techniques tailored to different sub-constellations, excluding the quasi-orthogonal sub-constellation, which hasbeen handled separately.The receiving device 402 identifies the modulation scheme used in each user-specific signal and applies correspondingdemapping algorithms. The algorithms are designed to reverse the modulation process and decode the received symbols intooriginal bit sequences. The demapping process excludes the quasi-orthogonal sub-constellation signals, which have beenprocessed separately. The demapping process maintain clarity and separation between different types of signals processedwithin the receiving device 402. By accurately demapping each user-specific signal, the receiver ensures that the original databits are retrieved without errors or loss. Further demapping process supports various modulation schemes commonly used inin wireless communication systems, ensuring compatibility with different transmission technologies.The receiving device 402 is further configured to decode each user-specific signal to obtain a decoded sequence of bits usingthe FEC decoder 412. The decoding process involves using the FEC decoder 412 to recover the original sequence of bits fromeach user-specific signal. This process is crucial for ensuring data integrity and reliability in communication systems. The primary purpose of decoding using FEC is to correct errors that may have occurred during transmission, thereby reconstructingthe original sequence of bits accurately. The FEC decoder 412 first detects errors in the received demapped signal. Errordetection is essential to identify and locate errors introduced during transmission, which can corrupt the transmitted data. Forexample, a user-specific signal was transmitted as "101010", but due to noise and interference, the receiving device 402 receives"111010". Error detection identifies that there is an error in the second bit position. Once errors are detected, the FEC decoder412 applies error correction algorithms to reconstruct the original sequence of bits. Error correction ensures that the receivercan accurately recover the original data despite errors in the received signal. Using a Reed-Solomon code, if the received signal"111010" has an error, the FEC decoder 412 can correct it to match the transmitted sequence "101010". The FEC Decoder 412employs specific decoding algorithms based on the FEC scheme used. Different FEC schemes have unique algorithms tailoredto their error correction capabilities and performance characteristics. The final output of the decoding process is a decodedsequence of bits. This sequence represents the reconstructed original data that was transmitted by the user, corrected for errorsintroduced during transmission. After applying the appropriate FEC decoding algorithm, the receiver obtains the correct sequence of bits "101010" from the received signal "111010".The receiver device 402 is further configured to merge the decoded sequence of bits with the user group index to obtain thedecoded message. The merger 414 receives the decoded sequence of bits from the FEC decoder 412 and the user group indexis extracted. These inputs are necessary for forming a coherent decoded message that accurately represents the original transmitted data. Both the decoded sequence of bits and the user group index are temporarily stored in buffers within the merger414. Buffering ensures that the inputs are aligned and synchronized for the merging process. The merger 414 combines thebuffered decoded sequence of bits with the corresponding user group index. The combination ensures that the data from eachuser is correctly attributed to its respective user group. The combination may be done by appending, prepending, or embeddingthe user group index within the decoded sequence of bits, depending on the implementation. The output of the merger 414 is acomplete decoded message that includes both the user-specific data and its associated user group index. This ensures that thereceiving device 402 can correctly interpret which user group the data belongs to, maintaining the context and coherence of thetransmitted information. There is provided a computer program comprising instructions that, when executed by a computer system, cause the computersystem to implement a method for operating the receiving device 402. In an example, the instructions are implemented on thecomputer-readable media, which include, but are not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), a computer-readable storage medium, and / or CPU cache memory. Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising","incorporating", "have", "is" used to describe, and claim the present disclosure are intended to be construed in a non-exclusivemanner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciatedthat certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, mayalso be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.

Claims

CLAIMS1. A transmitter device (102) comprising:a user group index extractor module (106) which receives an input sequence of bits (104) and extracts from the input sequence of bits a subsequence of bits corresponding to a user group index; aforward error correction encoder (108) which receives an input of a remaining sequence of bits after the subsequenceof bits corresponding to the user group index has been extracted, and generates a coded sequence of bits from the remaining sequence of bits; asplitter (110) which receives as an input a coded sequence of bits which is output of the forward error correctionencoder, and which generates a plurality of subsequences of coded bits by splitting the input coded sequence of bits;a plurality of subconstellation vector mapper units (112), wherein each one of the plurality of subconstellation vector mapper receives a respective one of the plurality of subsequences of coded bits output from the splitter and modulates the respective one of the plurality of subsequences of coded bits to generate a plurality of respective subconstellation symbol vectors; and aquasi-orthogonal subconstellation vector mapper unit (114) which receives the subsequence of bits corresponding tothe user group index, and modulates the received subsequence to generate a quasi-orthogonal subconstellation symbol vector; and arank one tensor unit (116) which generates a symbols vector by calculating the Kronecker product of the plurality ofmodulated subconstellation symbol vectors and the quasi-orthogonal subconstellation symbol vector.

2. The device (102) of claim 1, wherein the plurality of subconstellation vector mapping units (112) are non-coherentmodulation units.

3. The device (102) of claim 1, wherein the quasi-orthogonal subconstellation vector mapping unit (114) comprises atleast the vectors of the canonical basis and vectors from one of Galois sequences, Hadamard sequences, random Gaussian sequences, Grassmannian sequences, Zadoff-Chu sequences, standard legacy preamble sequences, demodulation reference signal, DMRS, sequences, sparse orthogonal vectors, and arbitrary permutations and rotations of any of the previous sequences.

4. The device (102) of claim 1, wherein the device is adapted for use in an environment where a plurality of transmittingdevices transmits messages in a random-access manner, where a random number of transmitters are active at a given time.

5. The device (102) of claim 1, wherein the device is adapted for use in an environment where users transmit messages ina grant free fashion, where transmitters transmit messages to a receiver without any prior request.

6. The device (102) of claim 1, wherein the device is adapted for use in a Massive Machine Type Communications,mMTC, environment.

7. The device (102) of claim 6, wherein the device is adapted for use in an mMTC environment which is an Internet ofThings, IoT, environment.

8. The device (102) of claim 1, wherein the device is adapted for use in an environment where a plurality of transmittersis simultaneously transmitting a sequence of bits to a single multi-antenna receiver in a Non-Orthogonal Multiple Access, NOMA, fashion.

9. A method (200) of operating a transmitter device (102) comprising steps of:receiving, at a user group index extractor module (106), an input sequence of bits and extracting from the input sequenceof bits a subsequence of bits corresponding to a user group index; receiving at a forward error correction encoder (108), an input of a remaining sequence of bits after the subsequence ofbits corresponding to the user group index has been extracted, and generating a coded sequence of bits from the remaining sequence of bits; receiving at a splitter (110), a coded sequence of bits which is output of the forward error correction encoder (108) andgenerating a plurality of subsequences of coded bits by splitting the input coded sequence of bits; receiving at a plurality of subconstellation vector mapper units (114), wherein each one of the plurality ofsubconstellation vector mapper receives a respective one of the plurality of subsequences of coded bits output from the splitter (110), and modulating the respective one of the plurality of subsequences of coded bits to generate a pluralityof respective subconstellation symbol vectors; and receiving at a quasi-orthogonal subconstellation vector mapper unit (114), the subsequence of bits corresponding to theuser group index, and modulating the received subsequence to generate a quasi-orthogonal subconstellation symbolvector; and generating, at a rank one tensor unit (116), a symbols vector by calculating the Kronecker product of the plurality ofmodulated subconstellation symbol vectors and the quasi-orthogonal subconstellation symbol vector.

10. The method (200) of claim 9, wherein the plurality of subconstellation vector mapping units (112) are non-coherentmodulation units.

11. The method (200) of claim 9, wherein the quasi-orthogonal subconstellation vector mapping unit (114) comprises atleast the vectors of the canonical basis and vectors from one of Galois sequences, Hadamard sequences, random Gaussian sequences, Grassmannian sequences, Zadoff-Chu sequences, standard legacy preamble sequences, demodulation reference signal, DMRS, sequences, sparse orthogonal vectors, and arbitrary permutations and rotations of any of the previous sequences.

12. The method (200) of claim 9, wherein the device (102) is adapted for use in an environment where a plurality oftransmitting devices transmits messages in a random-access manner, where a random number of transmitters are active at a given time.

13. The method (200) of claim 9, wherein the device (102) is adapted for use in an environment where users transmitmessages in a grant free fashion, where transmitters transmit messages to a receiver without any prior request.

14. A computer program comprising instructions for carrying out all the steps of the method according to any precedingmethod claim, when said computer program is executed on a computer system.

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