Low Density Lattice Multiple Access for 5G Capacity
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Solution Overview
Problem
Current 4G wireless networks face limitations in capacity due to modulation schemes, and fifth-generation (5G) wireless networks aim to achieve higher capacity densities, but existing sparse code multiple access (SCMA) technologies face challenges with increasing complexity and hardware requirements as the number of users grows.
Innovation Solution
The implementation of Low Density Lattice Multiple Access (LDLMA) using a shared multi-dimensional low-density lattice codebook for encoding user data, which allows for linear decoding complexity and reduces hardware requirements by using a Single-Input Single-Output (SISO) decoder, enabling efficient multi-user medium access and asymptotic approach to Shannon capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If joint multiuser detection is used to distinguish individual codewords of different users in SCMA, then user data can be separated and decoded, but the decoding complexity and hardware requirements increase significantly as the number of users grows
Solution Approach 1:
The received signal is segmented into multiple processing stages: initial signal reception, linear combination formation with optimized coefficients, lattice decoding of combined signals, and iterative interference cancellation. This segmentation transforms the complex joint detection problem into manageable sequential steps, reducing overall decoding complexity while maintaining user separation accuracy
Solution Approach 2:
Linear combinations of codewords serve as intermediary representations that simplify the detection process. By forming linear combinations with optimized coefficients before decoding, the system creates intermediate signals that are easier to decode than individual user signals, thereby reducing the complexity of the final user data separation while maintaining accuracy
2Productivity
If conventional modulation schemes are used in 4G networks, then implementation is simple and widely compatible, but capacity is limited and cannot approach Shannon bound
Solution Approach 1:
The system changes the fundamental parameter of signal representation from conventional modulation symbols to lattice codewords in multi-dimensional space. This parameter change enables the system to achieve capacity approaching Shannon bound by utilizing the geometric structure of lattice codes, while the low-density property of the lattice maintains implementation feasibility
Solution Approach 2:
The invention transitions from two-dimensional QAM constellations to multi-dimensional lattice structures. By embedding codewords in higher-dimensional spaces defined by the lattice geometry, the system achieves superior capacity efficiency and shaping gain, while the structured lattice framework provides algorithms that are computationally tractable
3Measurement precision
If SCMA uses distinct codebooks for each user, then user data can be distinguished at the receiver, but hardware requirements and processing complexity increase with the number of users
Solution Approach 1:
A single lattice codebook serves all users in the system, replacing the need for multiple user-specific codebooks. This universal codebook approach maintains user distinction capability through the structured lattice geometry and associated processing algorithms, while significantly reducing hardware resources by eliminating redundant codebook storage across multiple users
Data Source
AI summary
A method is provided of receiving user data from multiple transmitters, the user data from each transmitter having been encoded as a Low Density Lattice codeword, and the multiple Low Density Lattice codewords having been transmitted so as to be received as a combined signal at a receiver, the method of receiving comprising the steps of: (i) receiving the signal, (ii) calculating coefficients of linear combinations of the codewords from the multiple transmitters, (iii) calculating a scaling factor to be applied to the signal based on the coefficients, (iv) applying the scaling factor to the signal to provide a linear combination of the codewords, (v) decoding the linear combination of the codewords based on channel state information to obtain an optimal independent linear combination of user data, (vi) repeating steps (ii), (iii) (iv) and (v) to obtain at least as many optimal independent linear combinations as the number of transmitters, and recovering the user data from the optimal independent linear combinations.