Multi-mode OFDM Receiver with Reduced-Complexity ML Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communications methods and systems are overly power hungry and spectrally inefficient, particularly in high-capacity wireless and wireline communication systems, due to suboptimal Zero Forcing, SIC, and MMSE receivers, which are inferior to maximum likelihood receivers and suffer from increased constellation sizes and non-linear distortion.
Innovation Solution
The implementation of a multi-mode receiver using orthogonal frequency division multiplexing (OFDM) with reduced state/complexity maximum likelihood decoders, partial response signaling, and transmitter shaping filtering to reduce bandwidth usage and improve spectral efficiency, while supporting phase noise and non-linear distortion without pilot symbols, and employing cyclic filters to introduce inter-symbol correlation for improved detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Zero Forcing, SIC, or MMSE receivers are used, then device complexity is reduced, but spectral efficiency and reliability deteriorate due to suboptimal performance compared to maximum likelihood receivers
Solution Approach 1:
The maximum likelihood decoding process is segmented into multiple stages: syndrome computation, error pattern identification, and sequential correction. This segmentation allows the complex decoding task to be broken down into manageable steps that achieve near-ML performance with reduced computational complexity compared to exhaustive search methods
Solution Approach 2:
The receiver implements partial maximum likelihood decoding by focusing computational resources on the most probable error patterns rather than evaluating all possible combinations. This partial action approach achieves sufficient reliability for practical applications while avoiding the excessive complexity of complete maximum likelihood implementation
2Productivity
If larger constellation sizes are used to increase data rate, then spectral efficiency improves, but reliability worsens due to increased susceptibility to noise and distortion
Solution Approach 1:
The system employs iterative feedback between the decoder and equalizer, where decoding results are fed back to refine equalization parameters and vice versa. This feedback mechanism allows the system to maintain reliability with larger constellations by continuously adjusting to detected error patterns and improving signal reconstruction
Solution Approach 2:
The receiver performs preliminary syndrome computation and error pattern identification before final symbol decision. This preliminary action allows the system to anticipate and correct errors before they propagate, maintaining detection accuracy even with larger constellation sizes that are more vulnerable to noise
3Reliability
If non-linear distortion compensation is implemented to improve reliability, then performance in non-linear channels improves, but device complexity increases
Solution Approach 1:
The system uses self-service by leveraging the received signal structure and inherent redundancy to automatically characterize and compensate for non-linear distortion. The decoder identifies distortion patterns from the received signal itself without requiring external calibration or complex pre-characterization, achieving non-linear compensation with moderate complexity
Solution Approach 2:
The receiver dynamically adjusts decoding parameters and equalization coefficients based on detected channel conditions and distortion levels. By changing parameters adaptively rather than using fixed complex compensation algorithms, the system achieves good non-linear channel performance with controlled complexity
4Reliability
If pilot symbols are used to support non-linear distortion and phase noise, then reliability improves, but spectral efficiency deteriorates due to bandwidth occupation by pilot symbols
Solution Approach 1:
The system extracts channel and distortion information directly from the data-bearing signal structure rather than relying on separate pilot symbols. By taking out the necessary reference information from the data stream itself through clever signal design and processing, the system maintains reliability without sacrificing spectral efficiency to dedicated pilot overhead
5Productivity
If bandwidth is reduced to occupy half the bandwidth of conventional signaling, then spectral efficiency improves, but measurement precision worsens due to reduced signal power and increased noise impact
Solution Approach 1:
The system transitions from traditional time-domain or frequency-domain spreading to a combined time-frequency-code domain approach. By utilizing code dimension in addition to time and frequency, the system achieves spectral compression while maintaining detection precision through the added dimensional diversity that provides noise robustness
Solution Approach 2:
The signal employs composite modulation combining multiple modulation schemes and coding strategies in a unified framework. This composite approach allows the system to pack more information into reduced bandwidth while maintaining precision through the synergistic effects of different modulation and coding techniques working together
Data Source
AI summary
A receiver may comprise a sequence estimation circuit and operate in at least two modes. In a first mode, the sequence estimation circuit may process OFDM symbols received on a first number of data-carrying subcarriers to recover a number of mapped symbols per OFDM symbol that is greater than the first number. In a second mode, the sequence estimation circuit may process OFDM symbols received on a second number of data-carrying subcarriers to recover a number of mapped symbols per OFDM symbol that is equal to the second number. The second number may be equal to or different from the first number. While the receiver operates in the first mode, the sequence estimation circuit may be operable to generate candidate vectors and process the candidate vectors using a controlled ISCI model to generate reconstructed physical subcarrier values.