On Optimal TCM Encoders

An asymptotically optimal trellis-coded modulation (TCM) encoder requires the joint design of the encoder and the binary labeling of the constellation. Since analytical approaches are unknown, the only available solution is to perform an exhaustive search over the encoder and the labeling. For large constellation sizes and/or many encoder states, however, an exhaustive search is unfeasible. Traditional TCM designs overcome this problem by using a labeling that follows the set-partitioning principle and by performing an exhaustive search over the encoders. In this paper we study binary labelings for TCM and show how they can be grouped into classes, which considerably reduces the search space in a joint design. For 8-ary constellations, the number of different binary labelings that must be tested is reduced from 8!=40320 to 240. For the particular case of an 8-ary pulse amplitude modulation constellation, this number is further reduced to 120 and for 8-ary phase shift keying to only 30. An algorithm to generate one labeling in each class is also introduced. Asymptotically optimal TCM encoders are tabulated which are up to 0.3 dB better than the previously best known encoders

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

Optimization and Applications of Modern Wireless Networks and Symmetry

Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications

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

Energy-Efficient Symbol-Level Precoding in Multiuser MISO Based on Relaxed Detection Region

This paper addresses the problem of exploiting interference among simultaneous multiuser transmissions in the downlink of multiple-antenna systems. Using symbol-level precoding, a new approach towards addressing the multiuser interference is discussed through jointly utilizing the channel state information (CSI) and data information (DI). The interference among the data streams is transformed under certain conditions to a useful signal that can improve the signal-to-interference noise ratio (SINR) of the downlink transmissions and as a result the system's energy efficiency. In this context, new constructive interference precoding techniques that tackle the transmit power minimization (min power) with individual SINR constraints at each user's receiver have been proposed. In this paper, we generalize the CI precoding design under the assumption that the received MPSK symbol can reside in a relaxed region in order to be correctly detected. Moreover, a weighted maximization of the minimum SNR among all users is studied taking into account the relaxed detection region. Symbol error rate analysis (SER) for the proposed precoding is discussed to characterize the tradeoff between transmit power reduction and SER increase due to the relaxation. Based on this tradeoff, the energy efficiency performance of the proposed technique is analyzed. Finally, extensive numerical results show that the proposed schemes outperform other state-of-the-art techniques.Comment: Submitted to IEEE transactions on Wireless Communications. arXiv admin note: substantial text overlap with arXiv:1408.470

Flexible digital modulation and coding synthesis for satellite communications

An architecture and a hardware prototype of a flexible trellis modem/codec (FTMC) transmitter are presented. The theory of operation is built upon a pragmatic approach to trellis-coded modulation that emphasizes power and spectral efficiency. The system incorporates programmable modulation formats, variations of trellis-coding, digital baseband pulse-shaping, and digital channel precompensation. The modulation formats examined include (uncoded and coded) binary phase shift keying (BPSK), quatenary phase shift keying (QPSK), octal phase shift keying (8PSK), 16-ary quadrature amplitude modulation (16-QAM), and quadrature quadrature phase shift keying (Q squared PSK) at programmable rates up to 20 megabits per second (Mbps). The FTMC is part of the developing test bed to quantify modulation and coding concepts

Multidimensional Trellis Coded Phase Modulation Using a Multilevel Concatenation Approach

The first part of this paper presents a simple and systematic technique for constructing multidimensional M-ary phase shift keying (MMK) trellis coded modulation (TCM) codes. The construction is based on a multilevel concatenation approach in which binary convolutional codes with good free branch distances are used as the outer codes and block MPSK modulation codes are used as the inner codes (or the signal spaces). Conditions on phase invariance of these codes are derived and a multistage decoding scheme for these codes is proposed. The proposed technique can be used to construct good codes for both the additive white Gaussian noise (AWGN) and fading channels as is shown in the second part of this paper