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This letter introduces a novel joint power-allocation (PA) and relay beamforming optimization (JPBO) algorithm for non-orthogonal multiple-access (NOMA) amplify-and-forward (AF) relay systems to achieve the maximum weighted sum-rate (WSR). In our proposed new framework, the WSR maximization problem is transformed into a tractable weighted-sum minimum mean-square-error (WSMMSE) problem. Then, the PA and the relay beamforming-matrix (BM) are alternately optimized by use of successive convex approximation (SCA) and Lagrange-multiplier (LM) algorithm. Simulation results demonstrate that our proposed new algorithm can significantly improve the WSR with a faster convergence than the existing method.

A novel pulse-shaping scheme using multiwavelets (MWs) for the non-orthogonal multiple-access (NOMA) communication systems is proposed in this letter. Different from the conventional pulse-shaping schemes, this work exploits multiwavelets for constructing pulse-shaping filters so different users’ information can be multiplexed using the power diversity within the same frequency band. Hence, all users’ transmitted signals can be combined through the superposition by various power-allocation coefficients. At the proposed MW-NOMA receiver, after the successive interference cancellation (SIC) is carried out, the corresponding MW matched filters (shaping pulses) will be invoked to extract each individual user’s transmitted signal. A link-level performance evaluation of the proposed new NOMA scheme is also conducted in this letter. The simulation results demonstrate that our proposed new NOMA technique can significantly improve the system capacity and the bit error rate (BER) performance over the conventional NOMA scheme. When the power-allocation ratio is set to 0.4:0.6 in the proposed NOMA system for two users, the remarkable transmitting $E_{ \text {b}}/\mathcal {N}_{0}$ gain of 15 dB can be achieved by both users.

A novel robust pilot-tone spotting (PTS) technique for software defined radio (SDR) is introduced in this letter. This proposed new technique is designed upon the new idea of spectrum clustering. Given the spectrum of a band-limited signal, the pilot appears to be a spectral impulse located at the valley between two signal-spectrum segments. Then, the pilot-tone search is carried out thereby to first characterize two spectral segments around the pilot in the frequency domain and then find the pilot-tone frequency within the middle spectral segment. Based on the new PTS algorithm, the pilot signal embedded in the frequency-modulation (FM) stereo signal can be spotted accurately, even in the very low signal-to-noise-ratio conditions. The software-defined radio platform has been utilized to undertake real-world experiments for performance evaluation. Experimental results show that the performance of the proposed new PTS scheme is very promising.

An innovative automatic modulation classification via graph-based constellation analysis for ${M}$ -ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the ${M}$ -QAM signals within the modulation candidate set is first constructed and exploited to transform the received ${M}$ -QAM signal into graph domain. The concise graph representation of the received ${M}$ -QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) play the crucial roles in the fifth generation wireless communication systems. However, the physical-layer security still remains quite challenging. In this letter, a downlink cascaded transmitting zero-forcing-beamforming (ZFBF) technique is proposed to secure communications in a two-cell multiple-input multiple-output (MIMO) NOMA-based CRN. The proposed technique protects the information from illegitimate users (eavesdroppers) within the same and adjacent cells. The restriction on the number of at the transmitter can also be relaxed by applying the signal alignment for the ZFBF technique. This proposed new method can maximize the total secrecy rate of the MIMO-NOMA-based CRNs in the lack of conventional transceiver beamforming techniques. Numerical and simulation evaluations have demonstrated the effectiveness of the proposed method.

In this letter, a novel graph-based adequate and concise information representation paradigm is explored. This new signal representation framework can provide a promising alternative for manifesting the essential structure of the communication signals. A typical application, namely, band-limited signal detection, can thus be carried out using our proposed new graph-based signal characterization. According to Monte Carlo simulation results, the proposed graph-based signal detection method leads to the outstanding performance, compared with other existing techniques especially when the signal-to-noise ratio is rather small.

A novel automatic modulation classification method based on the graph presentation of the cyclic spectrum is proposed. In our proposed scheme, the periodicity and the symmetry of the cyclic spectrum will be exploited to establish a concise feature representation of multiple graphs. The modulated signal is first transformed from the cycle-frequency domain into the graph domain. Consequently, the concise graph-presentation, namely, a set of weighted directed rings, will be formulated as the robust features of the original signal. Those features can be easily expressed by the corresponding adjacency matrices. It can be verified that the adjacency matrices are sparse and the non-zero entries therein can be registered as the efficient feature parameters. Through the Hamming distance measure to enumerate the difference between the feature parameters resulting from the training data and the test data, one can perform the modulation classification. Monte Carlo simulation results demonstrate that our proposed method can achieve much better classification accuracy than the existing technique when the cyclic spectrum is used.

Low-density parity-check (LDPC) codes are favorable in low bit-error-rate and high code-rate applications. However, the decoding complexity for LDPC codes is quite large, especially for nonbinary LDPC codes. In this paper, we propose a new T-tolerance stopping criterion for LDPC decoders by exploiting the fact that the total aposteriori probability (APP) should increase across iterations when message passing (MP) algorithms are employed. Simulation results demonstrate that our proposed new T-tolerance criterion can greatly reduce the average iteration number (complexity) while the decoding performance degradation is controlled within 0.1 dB in the low bit-energy-to-noise-spectral-density ratio (Eb/N0) scenarios.

In this paper, the problem involving joint blind frame synchronization and encoder identification for binary low-density parity-check (LDPC) codes of binary phase-shift keyed (BPSK) signals is tackled. The unknown encoder and time-delay can be blindly estimated simultaneously using the average log-likelihood ratios (LLRs) of syndrome a posteriori probability (APP). A new two-stage search method with a search step of size q by exploiting the quasi-cyclic nature of the parity-check matrix is devised to reduce the complexity of the blind frame synchronization algorithm. Numerous Monte Carlo simulations are evaluated to demonstrate the effectiveness of the new joint blind frame-synchronization and encoder-identification scheme for multi-path situations.

Prevalent adaptive modulation and coding (AMC) techniques can facilitate the flexible strategies subject to the dynamic channel quality. It would be quite intriguing for one to build a blind encoder identification technique without spectrum-efficiency sacrifice for AMC transceivers. In this paper, we make the first-ever attempt to tackle the blind nonbinary low-density parity-check (LDPC) encoder identification given a predefined encoder candidate set over the Galois field GF(q) for q-ary quadrature amplitude modulation (q-QAM) signals. Our proposed method establishes the log-likelihood ratios (LLRs) of syndrome a posteriori probabilities (APPs), which specify the potential correctness of the underlying parity-check relations, and identifies the nonbinary LDPC encoder leading to the maximum average LLR over the candidate set. Monte Carlo simulation results verify the effectiveness of our proposed new scheme.

A novel blind parameter-estimation method, which identifies a turbo encoder, is proposed in this paper. The blind estimator is designed using an iterative expectation-maximization (EM) algorithm. To facilitate this innovative blind estimation scheme, we transform the recursive systematic convolutional (RSC) encoder into a non-systematic convolutional encoder preceded by a feedback encoder. The effect of the separate feedback encoder on the state sequence of the forward convolutional encoder will be studied. Besides, the effectiveness of our proposed new scheme will be evaluated by Monte Carlo simulations.

We propose a new QAM constellation called A16QAM. This constellation leads to the better average SER and the better CFM than the existing S16QAM, while having the same OFDM PMEPR upper-bounds as S16QAM. In addition, the OFDM PMEPR built by A16QAM is upper bounded by 4.0 and 2.0, if the information symbols are coded using Golay sequences and Golay complementary pairs, respectively.