In recent years, wideband spectrum sensing combined with sub-Nyquist sampling and compressed sensing technology in the field of cognitive radio has received widespread attention. However, the existing broadband detection methods based on sub-Nyquist sampling do not fully consider the spectrum feature changes caused by the sampling structure, resulting in unnecessary computational complexity in the support reconstruction process. In this paper, a novel reconstruction algorithm, called nearest orthogonal matching pursuit (N-OMP), is proposed based on modulated wideband converter (MWC) sub-Nyquist sampling structure. This algorithm utilizes the special power spectrum slicing characteristics caused by pseudo-random sequence and low-pass filtering. After an occupied subband is detected, it calculates the correlation coefficient between the residual vector and the column vectors corresponding to two adjacent subbands, based on which we can directly judge the occupancy of two adjacent subbands by comparing the size of the two correlation coefficients, thereby reducing the number of iterations of the reconstruction algorithm. Theoretical derivation and simulation experiment results show that, compared with the orthogonal matching pursuit (OMP) algorithm, the proposed algorithm can reduce the computational complexity by up to 50%, while showing better support reconstruction accuracy.

In the scenario where multiple device-to-device (D2D) users and cellular users coexist, the large number of D2D users not only results in the shortage of spectrum resources but also brings interference to the communication of cellular users. In this paper, we establish a clustering model centered on cellular users and propose a resource allocation algorithm based on a D2D clustering model. On the basis of ensuring the throughput requirements of cellular users, this algorithm reconstructs various matrices in the graph theory through probability models, sets the priority of D2D pairs, and maximizes the number of user accesses under interference tolerance. Besides, on the optimization of the number of users, we adopt the Rubinstein game model to adjust the game order according to the priority and optimize the bandwidth allocation mode, so as to improve the overall throughput of the network. Simulation results show that the proposed algorithm can increase the number of users and the network throughput, while shortening the spectrum allocation time by more than half.

Dual-functional radar-communication (DFRC) systems have gained attention for their advantages in spectrum sharing and hardware integration. Frequency-hopping (FH) MIMO radar waveforms enable communication via index modulation but introduce challenges such as increased range sidelobes, suboptimal communication performance, and incompatibility with conventional MIMO detection. Based on these considerations, we propose a new DFRC waveform design that combines frequency-hopping code selection (FHCS), binary frequency-shift keying (BFSK), and permutation selection (PS), termed FHCS-BFSK-PS. This design utilizes FH code vectors, frequency codes, and carrier-to-antenna mapping matrices as modulation parameters, embedding communication information across three dimensions: FH code index, frequency, and spatial phase. For communications, leveraging the weak coupling among these parameters, a simple and efficient demodulation algorithm based on the matched filtering criterion is proposed. For radar detection, we analyze the impact of fast-time domain FH on conventional MIMO detection and propose a modified detection method. This approach calculates and sums the log-likelihood ratios across multiple subpulses, thereby avoiding the incoherent accumulation of steering vectors in the subpulse domain. Finally, simulation results demonstrate the feasibility of the proposed scheme in target detection and highlight its superior performance in terms of symbol error rate (SER), data rate, and ambiguity function characteristics.

During moving target detection in Frequency Diverse Array multiple-input multiple-output (FDA-MIMO) radar, the speed and acceleration will result in range migration (RM), range curvature (RC), and Doppler frequency migration (DFM) of the received signal. Besides, due to the frequency offset across adjacent array elements, serious Doppler spread (DS) will occur between receiving channels, making it hard to be coherently accumulated and dramatically reducing the detection performance of the system. Aiming at this issue, firstly, slow time reversal transform (STRT) is used for received signal processing after match filtering, to eliminate the effects of acceleration. While suppressing the blind speed side lobe (BSSL) based on frequency offset, Radon-Fourier Transform (RFT) is adopted to remove RM and DS, thus estimating the speed of moving targets. Then, a speed filter and a series of acceleration filters are generated to compensate phase, allowing the target to accumulate coherently in rang-acceleration space. Simulation results reveal that the proposed method effectively resolves DS and mitigates effects caused by speed and acceleration, thereby improving detection performance while maintaining low computational burden.