SCMA Multi-Level Decoding with Power Imbalance for Lower BER
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Solution Overview
Problem
Traditional PLDPC-coded SCMA schemes face high bit-error-rate (BER) due to equal transmission power allocation and independent decoding processes for users, failing to effectively eliminate inter-user interference.
Innovation Solution
A power-imbalanced multi-level decoding (PI-MLD) method for SCMA, involving user classification into levels, progressive multi-level power optimization, and power-oriented decoding to determine a locally optimal power vector, followed by SCMA detection and decoding for each level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If equal transmission power allocation is used for all users, then the system is simple to implement, but inter-user interference cannot be effectively eliminated resulting in high BER
Solution Approach 1:
The patent segments users into different levels (first level and second level) based on the factor graph matrix structure. This segmentation allows different power allocation strategies to be applied to different user groups, enabling effective interference management while maintaining system simplicity. Users are divided into levels according to their connectivity patterns in the factor graph, creating a structured approach to power allocation.
Solution Approach 2:
The patent applies local quality by allocating different power levels to different user groups. Specifically, users in the first level are allocated higher power while users in the second level are allocated lower power. This localized power differentiation targets specific interference patterns in the system, effectively eliminating inter-user interference where it matters most while keeping the overall system manageable.
2Reliability
If independent decoding processes are used for each user, then the decoding process is simple, but inter-user interference remains uneliminated leading to high BER
Solution Approach 1:
The patent segments the decoding process into level-specific decoding operations. Instead of treating all users independently or all users together, the system performs decoding separately for first-level users and second-level users. This segmented approach allows the system to exploit the structured interference patterns revealed by the factor graph matrix, effectively eliminating inter-user interference while keeping each decoding stage manageable.
Solution Approach 2:
The patent performs preliminary classification of users into levels before the decoding process based on the factor graph matrix structure. This preliminary action organizes users in a way that anticipates and prepares for the decoding stage, allowing the subsequent level-specific decoding to efficiently eliminate interference. The factor graph analysis is performed beforehand to establish the user hierarchy that guides the decoding process.
3Reliability
If power-imbalanced allocation is implemented to eliminate inter-user interference, then BER is reduced, but the power optimization becomes more complex
Solution Approach 1:
The patent segments the power optimization problem into two distinct parts: power allocation for first-level users and power allocation for second-level users. This segmentation transforms a complex global optimization problem into two simpler, more manageable sub-problems. The factor graph matrix provides the structural basis for this segmentation, revealing natural groupings that simplify the optimization task.
Solution Approach 2:
The patent changes the power parameter differently for different user levels. By introducing power imbalance as a controlled parameter change, the system can optimize performance for each user level independently. The power allocation parameters are adjusted based on the user level classification, creating a structured approach to parameter optimization that reduces overall complexity.
4Reliability
If multi-level decoding is performed sequentially for each level, then inter-user interference is eliminated improving BER, but the decoding time increases
Solution Approach 1:
The patent segments the decoding time into distinct phases corresponding to different user levels. By organizing decoding into level-specific stages, the system processes users in an order that exploits the factor graph structure. This segmentation allows parallel processing opportunities and avoids redundant computations that would occur in a fully sequential approach, thereby reducing overall decoding time while maintaining the interference elimination benefits.
Data Source
AI summary
A power-imbalanced multi-level decoding method for sparse code multiple access includes: encoded bits of all users are mapped to multi-dimensional sparse codewords through predetermined codebooks; a factor graph matrix is constructed using the predetermined codebooks, all users are classified according to the factor graph matrix under predetermined constraints to determine Z levels; based on a predetermined total transmission power, a progressive multi-level power optimization algorithm is employed to perform power-imbalanced allocation for all users according to the Z levels, thereby determining a locally optimal power vector; transmission signals corresponding to the multi-dimensional sparse codewords are transmitted according to the locally optimal power vector; SCMA detection and power-oriented decoding are sequentially performed for users each level, and outputs decoded bit sequences for the L levels.