CNN demodulation model with cascade parallel crossing for CPM signals

The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power efficiency.However,the demodulation performance of the traditional maximum likelihood sequence detection(MLSD)algorithm significantly deteriorates in non-ideal synchronization or fading channels.To address this issue,this work proposes a convolutional neural network(CNN)called the cascade parallel crossing network(CPCNet)to enhance the robustness of CPM signals demodulation.The CPCNet model employs a multiple parallel structure and feature fusion to extract richer features from CPM signals.This approach constructs feature maps at different levels,resulting in a more comprehensive training of the model and improved demodulation performance.Simulation results show that under Gaussian channel,the proposed CPCNet achieves the same bit error rate(BER)performance as MLSD method when there is no timing error,but with 1/4 symbol period timing error,the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term memory deep neural network(CLDNN).In addition,under Rayleigh channel,the BER of the proposed method is reduced by 5%-87%compared to that of MLSD in the wide signal-to-noise ratio(SNR)region.

Improved iterative convergence method in Q-ary LDPC coded high order PR-CPM

The Q-ary low-density parity-check（LDPC） coded high order partial response continuous phase modulation（PR-CPM） with double iterative loops is investigated. This scheme shows significant improvements in power and bandwidth efficiency, but at the expense of long iterative decoding delay and computational complexity induced by the improper match between the demodulator and the decoder. To address this issue, the convergence behavior of Q-ary LDPC coded CPM is investigated for the Q=2 and Q〉2 cases, and an optimized design method based on the extrinsic information transfer chart is proposed to improve the systematic iterative efficiency. Simulation results demonstrate that the proposed method can achieve a perfect tradeoff between iterative decoding delay and bit error rate performance to satisfy real-time applications.

A New Two-Branch Amplification Architecture and its Application with Various Modulated Signals

This paper proposes a new two-branch amplification architecture that combines baseband signal decomposition with RF front-end optimization. In the proposed architecture, the filtered modulated signals are separated into two components that are then amplified independently and combined to regenerate an amplified version of the original signal. A branch with an efficient amplifier transmits a low-varying envelope signal that contains the main part of the information. Another branch amplifies the residual portion of the signal. The baseband decomposition and parameters of the RF part are optimized to find the configuration that gives the best power efficiency and linearity. For M-ary quadrature amplitude modulation （M-QAM） signals, this technique is limited in terms of power efficiency. However, for filtered continuous phase modulation （CPM） signals, especially for minimum shift keying （MSK） and Gaussian MSK （GMSK） signals, high power efficiency can be achieved with no significant impact on the overall linearity. The results show that this technique gives better performance than the single-ended ctass-B amplifier.

A reduced state SISO iterative decoding algorithm for serially concatenated continuous phase modulation

In this paper, serially concatenated continuous phase modulation （SCCPM） system is analyzed and a reduced state soft input soft output （SlSO） a posteriori probability algorithm is proposed. Based on the reduced state sequence detection （RSSD）, it has the more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number, thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the performance degradation is little with proper reduction scheme.

Low-complexity detection based on maximum cumulative measure and Laurent decomposition for multi-h CPM signal

In order to solve the problem of high computational complexity in demodulation for multi-h continuous phase modulation(CPM) signal, a maximum cumulative measure combing with the Laurent decomposition(MCM-LD) scheme is proposed to reduce the number of the grid states and the required number of matched filters, which degrades the demodulation complexity at the receiver.The advanced range telemetry(ARTM) Tier Ⅱ CPM signal is adopted to evaluate the performance in simulation. The results show that, compared with the traditional maximum likelihood sequence detection(MLSD), MCM-LD can respectively reduce the numbers of grid states and matched filters from 256 to 32 and 128 to 48 with negligible performance loss, which effectively degrades the computational complexity for multi-h CPM signal.