Figure 18 shows the performance of the 16-QAM HNN MLSE equalizer in a sparse channel of length, compared to its performance in dense channels of length to.
Figure 17 shows the performance of the BPSK HNN MLSE equalizer in a sparse channel of length, compared to its performance in dense channels of length to.
A sparse channel of length, where only % of the taps are nonzero, is compared to a dense channel of length, so that the number of nonzero taps in the sparse channel is equal to that in the equivalent dense channel.
Considering synthetic sparse signals of length N=120, with T=4 non-zero coefficients, we obtain measurements according to (14).
Step 1. Generate data: choose a dictionary and synthesize test signals by generating sparse vectors of length each, and computing for all.
These differences are unlikely to be due to collisions as the count vector as implemented in RDKit is a sparse vector of length 4294967296 bits.
Figure 13 shows the evenly spaced normalized nominal CIR taps of sparse channels of length, where % (a), % (b), % (c) and % (d), reducing the number of nonzero CIR taps to % of the CIR length.
Signal reconstruction algorithm is the core of compressed sensing theory, and it is the process of reconstruction sparse signal x (length N) by the help of the measurement vector (used M times).
Table 2 MSE for sparse signals of length N=120 obtained with CS, for variable number of measurements, m=15:5:35, and various types of sensing matrices m MSE Gaussian [26] [30] Alg. 3 Alg.
We assume x N × 1 is a k-sparse signal of length N.
