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In this letter, the 1-bit compressive sensing (CS) technology is utilized to recover joint sparse signal. In the context of multi-bits quantization, compared to the single measurement vector (SMV) case, the successful recovery rate can be significantly improved using multiple measurement vectors (MMV) model. Therefore, we introduce the MMV model for signal recovery from 1-bit sampling. Due to the amplitude information loss using 1-bit quantizing, a new sampling framework is proposed to estimate the l 2 -norm of each measurement vector for continuous-time signal, followed by the corresponding estimation algorithm 1-bit iterative hard thresholding (1-bit IHT). Then we develop the algorithm of binary IHT for MMV (M-BIHT) to recover joint sparse signal under uncertain noise. Compared to the 1-bit SMV model and multi-bits quantization MMV mode, the experimental results illustrate the recovery performance is improved greatly by the 1-bit MMV model.

Orthogonal frequency division multiplexing (OFDM) is a promising technique for dynamic spectrum access due to flexibility in spectrum shaping. However, out-of-band (OOB) power radiation of OFDM introduces significant interference to adjacent users. This is a serious problem in cognitive radio networks, which affects the secondary system to access the instantaneous spectrum hole. Existing methods either do not effectively reduce OOB power leakage or introduce significant bit-error-rate deterioration at the receiver. In this paper, a joint spectral precoding scheme is developed for OOB power reduction by using the matrix operations of orthogonal projection and singular value decomposition. We also propose an algorithm that designs the precoding matrix under receive performance constraint, which is then converted to matrix condition number constraint in practice. This method achieves the desirable spectrum envelope and receive performance by selecting zero-forcing frequencies. Simulation results show that OOB power decreases significantly by the proposed scheme under condition number constraint.

We study the problem of minimizing data aggregation time in wireless sensor networks (WSNs) under the practical duty-cycle scenario where nodes switch between active states and dormant states periodically for energy efficiency. Under the protocol interference model, we show that the problem is NP-hard and present a lower bound of delay for any data aggregation scheme. To solve the problem efficiently, we then construct a routing tree based on connected dominator set and propose two aggregation scheduling algorithms, which are the centralized Greedy Aggregation Scheduling (GAS) and the distributed Partitioned Aggregation Scheduling (PAS), so as to generate collision-free transmission schedules for data aggregation in duty-cycled WSNs. To minimize the total delay, GAS tries to achieve maximal concurrent transmissions in each time-slot during each frame by using global information, while PAS leverages a network partition based strategy and local information to ensure the largest degree of channel reuse across space and time domains. Theoretical analysis indicates that each algorithm consumes at most $$O(R+\varDelta)$$ O ( R + Δ ) frames and achieves nearly constant factor approximation on the optimal delay. Here R and $$\varDelta$$ Δ are the network radius and the maximum node degree, respectively. We also evaluate the practicability of our algorithms by extensive simulations under various network conditions and the results corroborate our theoretical analysis.

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.

This paper focuses on wide-sense stationary signal processing within a compressive sensing framework, proposing a new method of compressive sampling fast Fourier transform (FFT) accumulation method (CS-FAM). Depending on how it is applied, CS-FAM has one or two steps, allowing for versatility in multiband signal detection and parameter extraction. In the first step, the active sub-bands are detected using multiple measurement vectors (MMVs) and multiuser detection is achieved using a bandwidth constraint. In applications where it is required, such as in estimations of carrier frequency, symbol rate, or modulation format identification, the second step can be used to reconstruct the cyclic spectrums of each user individually. Based on the results of first step, parameter extraction is performed by searching for peaks in the cyclic spectrum rather than by the usual method of setting a threshold. Compared to other cyclic feature detection methods based on sub-Nyquist sampling, CS-FAM is low in complexity, allowing for practical implementation. Based on the results of the first step, parameter extraction from the cyclic spectrum is performed by searching for peaks rather than by setting a threshold. Although CS-FAM can only be employed for multiband signal detection, compared to other cyclic feature detection methods based on sub-Nyquist sampling, it is low in complexity, which makes practical implementation possible. Numerical simulations are presented to demonstrate the robustness of CS-FAM's multiband signal detection and the effectiveness of its cyclic spectrum estimation against both sampling rate reduction and noise uncertainty.

Data broadcast is a fundamental operation in wireless sensor networks (WSNs). The existence of wireless interference makes it nontrivial to design a minimum-latency broadcast scheme, which is known to be NP-hard. Existing works all assume strict time synchronization and provide centralized TDMA scheduling algorithms. However, WSNs in practice are more likely to be distributed asynchronous systems. In this paper, we investigate the problem of data broadcast with minimum latency for distributed asynchronous WSNs. To this end, we propose a Distributed Asynchronous Broadcast (DAB) algorithm which crucially leverages an elaborately optimized carrier-sensing range together with collision-backoff schemes to coordinate the transmissions among the nodes on a predetermined broadcast backbone. Theoretical analysis shows that DAB is order-optimal and achieves constant factor approximation to the optimal delay. We then conduct extensive simulations to evaluate the practical capability of DAB in asynchronous WSNs and the results corroborate our theoretical analysis.