SIC Ordering Algorithms for MIMO Stream Selection
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Solution Overview
Problem
Current wireless communication systems, particularly in LTE technology, face challenges in efficiently decoding multiple streams of data in MIMO systems due to the need for correct stream selection for interference cancellation, which can lead to unsuccessful decoding and resource constraints.
Innovation Solution
The method involves selecting a stream for decoding based on a metric that calculates the gap between measured and allocated SNR or rate, using SNR gap-based or rate gap-based metrics, and employing speculative single code block ordering to ensure robust and efficient interference cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional interference cancellation methods are used in MIMO systems, then decoding can be performed, but the decoding success rate is low and multiple decoding attempts are required
Solution Approach 1:
The patent applies preliminary action by calculating SNR gap metrics for all streams before performing interference cancellation. The receiver computes the metric Γ_i = SNR_i - γ̄_i (measured SNR minus allocated SNR) for each stream i, uses these metrics to determine the optimal decoding order, and then proceeds with interference cancellation in that order. This preliminary metric calculation and ordering step ensures that the most reliably decodable streams are decoded first, improving overall decoding success rate while reducing the number of retransmission attempts required.
2Reliability
If stream selection is performed without metric-based ordering, then the decoding process is simple, but interference cancellation effectiveness is reduced
Solution Approach 1:
The patent applies parameter changes by introducing an SNR gap metric parameter Γ_i that combines measured SNR (γ_i) and allocated SNR (γ̄_i) to create a new ordering criterion. Instead of using raw SNR values or simple stream indices, the system transforms these parameters into a gap metric that better predicts decoding success. The receiver calculates Γ_i for each stream, sorts streams by this metric, and uses the sorted order for interference cancellation. This parameter transformation significantly improves interference cancellation effectiveness while adding manageable computational complexity.
Data Source
AI summary
Certain aspects of the present disclosure provide ordering techniques for a Successive Interference Cancellation (SIC) receiver which may be used to robustly choose a correct stream for first decode under varying data rates, SNR and mobile propagation conditions in Multiple Input Multiple Output (MIMO) systems. The SIC ordering techniques discussed in the disclosure include SNR and/or Rate based information theoretic approach. For example, the SIC receiver may evaluate an SNR based or RATE-based information theoretic metric for the MIMO streams and choose one stream with a higher value of the metric for decoding first. A speculative single code block based approach is may also be used for selecting a stream for first decode, by leveraging the presence of per code block Cyclic Redundancy Check (CRC) and the lack of time diversity in LTE systems.