Achieving full diversity in multi-antenna two-way relay networks via symbol-based physical-layer network coding

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users’ modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large

Application of Convolutional Neural Network Framework on Generalized Spatial Modulation for Next Generation Wireless Networks

A novel custom auto-encoder Complex Valued Convolutional Neural Network (AE-CVCNN) model is proposed and implemented using MATLAB for multiple-input-multiple output (MIMO) wireless networks. The proposed model is applied on two dierent generalized spatial modulation (GSM) schemes: the single symbol generalized spatial modulation SS - GSM and the multiple symbol generalized spatial modulation (MS-GSM). GSM schemes are used with Massive-MIMO to increase both the spectrum eciency and the energy eciency. On the other hand, GSM schemes are subjected to high computational complexity at the receiver to detect the transmitted information. High computational complexity slows down the throughput and increases the power consumption at the user terminals. Consequently, reducing both the total spectrum eciency and energy eciency. The proposed CNN framework achieves constant complexity reduction of 22.73% for SSGSM schemes compared to the complexity of its traditional maximum likelihood detector (ML). Also, it gives a complexity reduction of 14.7% for the MS-GSM schemes compared to the complexity of its detector. The performance penalty of the two schemes is at most 0.5 dB. Besides to the proposed custom AE CV-CNN model, a dierent ML detector0s formula for SS -GSM schemes is proposed that achieves the same performance as the traditional ML detector with a complexity reduction of at least 40% compared to that of the traditional ML detector. In addition, the proposed AE-CV-CNN model is applied to the proposed ML detector,and it gives a complexity reduction of at least 63.6% with a performance penalty of less than 0.5 dB. An interesting result about applying the proposed custom CNN model on the proposed ML detector is that the complexity is reduced as the spatial constellation size is increased which means that the total spectrum eciency is increased by increasing the spatial constellation size without increasing the computational complexity

Multiple trellis coded modulation

A technique for designing trellis codes to minimize bit error performance for a fading channel. The invention provides a criteria which may be used in the design of such codes which is significantly different from that used for average white Gaussian noise channels. The method of multiple trellis coded modulation of the present invention comprises the steps of: (a) coding b bits of input data into s intermediate outputs; (b) grouping said s intermediate outputs into k groups of s.sub.i intermediate outputs each where the summation of all s.sub.i,s is equal to s and k is equal to at least 2; (c) mapping each of said k groups of intermediate outputs into one of a plurality of symbols in accordance with a plurality of modulation schemes, one for each group such that the first group is mapped in accordance with a first modulation scheme and the second group is mapped in accordance with a second modulation scheme; and (d) outputting each of said symbols to provide k output symbols for each b bits of input data

RADAR-EMBEDDED SATCOM WITH DEEP NEURAL NETWORK DEMODULATION

Approved for public release. Distribution is unlimited.In this work, the feasibility, design, and implementation of radar-embedded communications with satellite applications are investigated. We design a deep neural network (DNN) machine learning detector to demodulate SATCOM data. The performance result is compared with the detection method of using maximum likelihood estimation (MLE) to estimate the amplitude and phase of the radar signal, which is followed by a maximum likelihood detection (MLD) receiver. Pulsed radar and linear frequency modulation (LFM) waveforms are chosen to embed communications symbols. Quaternary phase-shift keying (QPSK) and eight phase-shift keying (8PSK) modulations are used for illustration. In this work, three DNN demodulators for radar-embedded communications are developed. One of the DNN detectors actually outperforms the MLD demodulator and is shown to be robust for pulsed radar-embedded communications. One of our goals is to embed satellite communications into LFM waveform, which is used in synthetic aperture radar (SAR). The DNN works well for LFM radar-embedded communications when the received LFM phase offset is removed a priori. However, the DNN symbol error rate (SER) performance suffers when the LFM phase offset is introduced for large RCR. Lastly, we perform laboratory transmission and reception tests: a) shielded cable and b) over-the-air (OTA) tests. It is shown that pulsed radar-embedded communication is feasible with both MLE-MLD and DNN detectors with reasonable SER performance.Lieutenant, United States Nav

Design Simulation of Multiple Differential Transceiver at 2.0 GHz for Third Generation Mobile Communication System

Third generation mobile communication system is widely used nowadays. One of its parameter standard, which is QPSK modulation has been adopted by International Telecommunication Union (ITU) to be used in IMT-2000. However, due to amplitude variations introduced in QPSK, a rather robust and reliable data modulation technique, namely the 7c/4-shift Differential QPSK is proposed. For detection purposes, two types of detectors are evaluated for their performance in AWGN and Rayleigh fading channels. A differential detection technique called multiple differential detection technique which uses maximum-likelihood sequence estimation (MLSE) of the transmitted phases is compared with conventional differential detection which uses symbol-bysymbol detection. By using some of the IMT-2000 standard parameters, the simulation results show that multiple differential detection scheme performs much better than conventional differential detection scheme

An analysis of carrier phase jitter in an MPSK receiver utilizing map estimation

The use of 8 and 16 PSK TCM to support satellite communications in an effort to achieve more bandwidth efficiency in a power-limited channel has been proposed. This project addresses the problem of carrier phase jitter in an M-PSK receiver utilizing the high SNR approximation to the maximum aposteriori estimation of carrier phase. In particular, numerical solutions to the 8 and 16 PSK self-noise and phase detector gain in the carrier tracking loop are presented. The effect of changing SNR on the loop noise bandwidth is also discussed. These data are then used to compute variance of phase error as a function of SNR. Simulation and hardware data are used to verify these calculations. The results show that there is a threshold in the variance of phase error versus SNR curves that is a strong function of SNR and a weak function of loop bandwidth. The M-PSK variance thresholds occur at SNR's in the range of practical interest for the use of 8 and 16-PSK TCM. This suggests that phase error variance is an important consideration in the design of these systems

RECEIVER DESIGN AND PERFORMANCE ANALYSIS FOR COMMUNICATION CHANNELS WITH CARRIER PHASE NOISE AND FADING

Ph.DDOCTOR OF PHILOSOPH

Automatic modulation classification of communication signals

The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know in advance if the received communication signal is analogue modulated or digitally modulated. Furthermore, it is noted that the applications of the currently existing AMR algorithms designed for handling both analog and digital communication signals are rather restricted in practice. Motivated by this, an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals is developed in this dissertation. The proposed algorithm is able to recognize the concrete modulation type if the input is an analog communication signal and to estimate the number of modulation levels and the frequency deviation if the input is an exponentially modulated digital communication signal. For linearly modulated digital communication signals, the proposed classifier will classify them into one of several nonoverlapping sets of modulation types. In addition, in M-ary FSK (MFSK) signal classification, two classifiers have also been developed. These two classifiers are also capable of providing good estimate of the frequency deviation of a received MFSK signal. For further classification of linearly modulated digital communication signals, it is often necessary to blindly equalize the received signal before performing modulation recognition. This doing generally requires knowing the carrier frequency and symbol rate of the input signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm have been developed. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. Unlike the cyclic correlation based estimators, it does not require the transmitted symbols being non-circularly distributed. The symbol rate estimator is based on digital communication signals\u27 cyclostationarity related to the symbol rate. In order to adapt to the unknown symbol rate as well as the unknown excess bandwidth, the received signal is first filtered by using a bank of filters. Symbol rate candidates and their associated confident measurements are extracted from the fourth order cyclic moments of the filtered outputs, and the final estimate of symbol rate is made based on weighted majority voting. A thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation

Dispensing with Channel Estimation…

In this article, we investigate the feasibility of noncoherent detection schemes in wireless communication systems as a low-complexity alternative to the family of coherent schemes. The noncoherent schemes require no channel knowledge at the receiver for the detection of the received signal, while the coherent schemes require channel inherently complex estimation, which implies that pilot symbols have to be transmitted resulting in a wastage of the available bandwidth as well as the transmission power