Non-orthogonal multiple access
Systems, methods, and apparatuses for analyzing and synthesizing wireless communication signals are provided. A receiver might transform a received signal into a basis in which the transformed signal is sparse, which can reduce the complexity of joint detection by facilitating message passing algorithm (MPA) decoding. A transmitter might employ dense codewords in a first basis, which may facilitate certain signal-processing operations and may provide a transmission with a low peak-to-average-power ratio. The codewords can be designed to be sparse when transformed to a second basis. The codewords may be configured for non-orthogonal multiple access.
1. A method for wireless communication, comprising:
receiving a multi-layer non-orthogonal multiple access (NOMA) communication, each layer in the multi-layer NOMA communication comprising a circular-shifted pulse waveform modulated with the each layer's direct-sequence code,
wherein the each layer's direct-sequence code filters spectral content of the circular-shifted pulse waveform to produce a NOMA sparsity pattern characterized by a set of active subcarriers that is unique to the each layer;
transforming the multi-layer NOMA communication into a plurality of subcarrier-frequency values; and
performing joint detection of data encoded in the plurality of subcarrier-frequency values, comprising identifying, in the plurality of subcarrier-frequency values, the each layer's NOMA sparsity pattern produced by the each layer's direct-sequence code.
2. The method of claim 1 , wherein performing joint detection employs at least one factor graph.
3. The method of claim 1 , wherein performing joint detection comprises despreading the each layer, wherein despreading employs an inverse, a transpose, or a Hermitian adjoint of an operator used to spread data onto the plurality of subcarrier-frequency values.
4. The method of claim 1 , wherein performing joint detection employs a codebook of NOMA sparsity patterns to determine which layers in the multi-layer NOMA communication are present and/or which data symbols are encoded in the multi-layer NOMA communication.
5. The method of claim 1 , wherein the each layer employs a plurality of different direct-sequence codewords to encode different data values, each of the plurality of different direct-sequence codewords producing a same NOMA sparsity pattern; and wherein performing joint detection comprises determining which of the plurality of different direct-sequence codewords is in the multi-layer NOMA communication.
6. The method of claim 1 , further comprising equalizing the plurality of subcarrier-frequency values before performing joint detection.
7. The method of claim 1 , wherein performing joint detection comprises employing at least one of a message-passing algorithm, maximum a posterior detection, sphere decoding, maximum likelihood detection, node pruning, successive interference cancellation, multi-user detection, and an artificial neural network.
8. An apparatus for wireless communication, comprising: a non-transitory computer-readable memory; and at least one processor coupled to the non-transitory computer-readable memory and configured for:
receiving a multi-layer non-orthogonal multiple access (NOMA) communication, each layer in the multi-layer NOMA communication comprising a circular-shifted pulse waveform modulated with the each layer's direct-sequence code,
wherein the each layer's direct-sequence code filters spectral content of the circular-shifted pulse waveform to produce a NOMA sparsity pattern characterized by a set of active subcarriers that is unique to the each layer;
transforming the multi-layer NOMA communication into a plurality of subcarrier-frequency values; and
performing joint detection of data encoded in the plurality of subcarrier-frequency values, comprising identifying, in the plurality of subcarrier-frequency values, the each layer's NOMA sparsity pattern produced by the each layer's direct-sequence code.
9. The apparatus of claim 8 , wherein performing joint detection employs at least one factor graph.
10. The apparatus of claim 8 , wherein performing joint detection comprises despreading the each layer, wherein despreading employs an inverse, a transpose, or a Hermitian adjoint of an operator used to spread data onto the plurality of subcarrier-frequency values.
11. The apparatus of claim 8 , wherein performing joint detection employs a codebook of NOMA sparsity patterns to determine which layers in the multi-layer NOMA communication are present and/or which data symbols are encoded in the multi-layer NOMA communication.
12. The apparatus of claim 8 , wherein the each layer employs a plurality of different direct-sequence codewords to encode different data values, each of the plurality of different direct-sequence codewords producing a same NOMA sparsity pattern; and wherein performing joint detection comprises determining which of the plurality of different direct-sequence codewords is in the multi-layer NOMA communication.
13. The apparatus of claim 8 , further comprising equalizing the plurality of subcarrier-frequency values before performing joint detection.
14. The apparatus of claim 8 , wherein performing joint detection comprises employing at least one of a message-passing algorithm, maximum a posterior detection, sphere decoding, maximum likelihood detection, node pruning, successive interference cancellation, multi-user detection, and an artificial neural network.
15. A computer program product, comprising: a non-transitory computer-readable memory having computer-readable program code stored thereon, the computer-readable program code containing instructions executable by one or more processors for:
receiving a multi-layer non-orthogonal multiple access (NOMA) communication, each layer in the multi-layer NOMA communication comprising a circular-shifted pulse waveform modulated with the each layer's direct-sequence code,
wherein the each layer's direct-sequence code filters spectral content of the circular-shifted pulse waveform to produce a NOMA sparsity pattern characterized by a set of active subcarriers that is unique to the each layer;
transforming the multi-layer NOMA communication into a plurality of subcarrier-frequency values; and
performing joint detection of data encoded in the plurality of subcarrier-frequency values, comprising identifying, in the plurality of subcarrier-frequency values, the each layer's NOMA sparsity pattern produced by the each layer's direct-sequence code.
16. The computer program product of claim 15 , wherein performing joint detection employs at least one factor graph.
17. The computer program product of claim 15 , wherein performing joint detection comprises despreading the each layer, wherein despreading employs an inverse, a transpose, or a Hermitian adjoint of an operator used to spread data onto the plurality of subcarrier-frequency values.
18. The computer program product of claim 15 , wherein performing joint detection employs a codebook of NOMA sparsity patterns to determine which layers in the multi-layer NOMA communication are present and/or which data symbols are encoded in the multi-layer NOMA communication.
19. The computer program product of claim 15 , wherein the each layer employs a plurality of different direct-sequence codewords to encode different data values, each of the plurality of different direct-sequence codewords producing a same NOMA sparsity pattern; and wherein performing joint detection comprises determining which of the plurality of different direct-sequence codewords is in the multi-layer NOMA communication.
20. The computer program product of claim 15 , further comprising instructions executable by one or more processors for equalizing the plurality of subcarrier-frequency values before performing joint detection.
21. The computer program product of claim 15 , wherein performing joint detection comprises employing at least one of a message-passing algorithm, maximum a posterior detection, sphere decoding, maximum likelihood detection, node pruning, successive interference cancellation, multi-user detection, and an artificial neural network.