Receiver Sparse Recovery for Multi-User IoT
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Solution Overview
Problem
In multi-user communication systems, especially those with a large number of IoT devices, conventional multi-user detection systems face challenges such as high computational complexity and energy consumption due to the need for a large number of correlators, and the impracticality of acquiring channel state information from all users, particularly when non-orthogonal spreading codes lead to packet collisions.
Innovation Solution
The system reduces the number of correlators by projecting the received signal onto a low-dimensional subspace spanned by the active users' spreading codes, using a minimum mean squared error (MMSE) matrix and a dimensionality-reduction matrix to compute updated correlator coefficients, allowing for sparse recovery of transmitted symbols without reducing the number of users, thus increasing the receiver's capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of correlators is increased to handle a large number of users, then the receiver can support more users, but the computational complexity and energy consumption increase excessively
Solution Approach 1:
The patent extracts only the necessary components for signal processing by using a reduced number of correlators equal to the number of active users rather than the total number of users. This extraction approach removes unnecessary correlators that would contribute to computational complexity while maintaining the ability to detect active users through sparse recovery techniques.
Solution Approach 2:
The patent transforms the problem from the time domain to the frequency domain using Fourier transform, and then applies dimensionality reduction by projecting the signal onto a lower-dimensional subspace. This dimensional transformation allows the system to handle a large number of users with fewer correlators by exploiting the sparsity of active users in the transformed domain.
2Quantity of substance
If the number of correlators is increased to handle a large number of users, then the receiver can support more users, but the energy consumption increases excessively
Solution Approach 1:
The patent extracts only the necessary components for signal processing by using a reduced number of correlators equal to the number of active users rather than the total number of users. This extraction approach removes unnecessary correlators that would contribute to energy consumption while maintaining the ability to detect active users through sparse recovery techniques.
Solution Approach 2:
The patent changes the parameter of correlator count from being proportional to the total number of users to being proportional to the number of active users. This parameter change is enabled by using sparse recovery algorithms that can identify active users from a reduced set of correlator outputs, thereby significantly reducing energy consumption in IoT devices.
3Reliability
If orthogonal spreading codes are used for each user, then packet collision can be avoided, but the number of users is limited due to the limited number of orthogonal codes
Solution Approach 1:
The patent moves from orthogonal frequency-division multiplexing (OFDM) to filter bank multi-carrier (FBMC) modulation, which provides better spectral containment and reduced inter-carrier interference. This dimensional change in modulation approach allows for more users to be supported while maintaining reliable packet detection even with non-orthogonal spreading codes, as the filter bank structure provides inherent interference mitigation.
Solution Approach 2:
The patent combines FBMC modulation with sparse recovery techniques to create a composite approach that overcomes the limitations of both individual methods. The FBMC provides robust signal separation while sparse recovery enables efficient detection of active users, together allowing support for a large number of users without requiring orthogonal codes for each user.
4Quantity of substance
If non-orthogonal spreading codes are used to support more users, then the number of users can be increased, but packet collisions occur and packet recovery becomes challenging
Solution Approach 1:
The patent implements an iterative feedback mechanism where the receiver first identifies active users through sparse recovery, then uses this information to refine the detection process. The feedback loop continuously improves packet recovery accuracy by updating the set of active users based on the detected signal characteristics, thereby reliably recovering packets even when non-orthogonal spreading codes cause collisions.
Solution Approach 2:
The patent performs preliminary identification of active users using sparse recovery techniques before attempting full packet recovery. This preliminary action separates the collision-affected signals from inactive users, making the subsequent packet recovery process much more manageable and reliable by focusing computational resources only on the active users' packets.
Data Source
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AI summary
A communication system includes a receiver to receive a signal with symbols encoded with a spreading code selected from a set of spreading codes, a filter to produce a filtered signal using a number of correlators less than a number of the spreading codes in the set of spreading codes, and a detector to detect the symbols transmitted by the transmitters from the filtered signal using sparse recovery with the dictionary matrix. The communication system also includes a processor to determine a minimum mean squared error (MMSE) matrix based on the set of spreading codes and a variance of noise in the channels, project the MMSE matrix to a low-dimensional space to produce a low-dimensional MMSE matrix, update the set of coefficients of set of correlators with the elements of the low-dimensional MMSE matrix, and update elements of a dictionary matrix based on the elements of the low-dimensional MMSE matrix.