Accurate and efficient foliage penetration (FOPEN) target recognition plays a vital role in many mission-critical applications, ranging from civilian to surveillance and military. Recently, device-free sensing (DFS), as an emerging technique, has gained great popularity because it requires no dedicated equipment other than wireless transceivers. Although some DFS-based approaches have been successfully applied in foliage environments, they are vulnerable to climate dynamics and heavily rely on re-labeling large amounts of new data when the weather is altered. To address this issue, a CNN-based weather adaptive target recognition network (WATRNet) is proposed in this paper. Specifically, a lightweight weather conditional normalization (WCN) module is embedded atop each convolutional block to encode inputs under different weather conditions into a shared latent feature space. Under an end-to-end learning manner, the proposed WATRNet first learns knowledge from sufficient labeled data under a certain weather condition to achieve a precise classifier. When applying this model under another weather condition, only the WCN module needs to be retrained using limited new labeled samples to learn weather-invariant features, while the rest convolutional parameters in WATRNet are frozen. Consequently, the domain discrepancy caused by climate variations can be adaptively mitigated with as few relabeled data as possible. Comprehensive evaluations are carried out on a real FOPEN dataset collected under four different weather conditions. Experimental results verify that the presented method can achieve over 90% accuracy, even when it implements from a normal weather condition to another severe weather condition with only small amounts of training samples.

Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input–multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.

In this letter, we propose a novel estimator for the phase parameters of sinusoidal frequency-modulated (SFM) signals. First, we developed a simple function to perform phase modulation on the original SFM signal to obtain a new modulated signal. Second, we derived the amplitude spectrum (AS) of the modulated signal and proved that the AS exhibited peaks periodically. Third, we obtained the parameter estimation of SFM signals by locating the AS peaks. Finally, the estimates were refined to reduce bias. The simulation results show that the proposed technique has a lower signal-to-noise ratio threshold than the existing methods.

A novel approach for the parameter estimation of polynomial phase signals (PPSs) is proposed. Unlike the existing methods, the proposed method uses the spectrum phase (SP) rather than the traditional instantaneous phase (IP) for parameter estimation. First, we present an unwrapping procedure for the SP. Second, we derive a method that can convert the unwrapped SP into the IP and then perform polynomial regression on the IP to obtain the estimates of the parameters. Finally, we use the O'Shea refinement strategy to refine the estimates to achieve the Cramér-Rao lower bound. The simulation results show that the proposed technique has a lower signal-to-noise ratio threshold and is less complex than the existing methods for high-order PPSs.

Group-sparsity-based method is applied to the 2qth-order difference coarray for underdetermined wideband direction of arrival (DOA) estimation. For complexity reduction, a focused compressive-sensing-based approach is proposed, without sacrificing its performance. Different from the conventional focusing approach, in the proposed one, focusing is applied to the virtual arrays and no preliminary DOA estimation is required. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

To reach a higher number of degrees of freedom by exploiting the fourth-order difference co-array concept, an effective structure extension based on two-level nested arrays is proposed. It increases the number of consecutive lags in the fourth-order difference coarray, and a virtual uniform linear array (ULA) with more sensors and a larger aperture is then generated from the proposed structure, leading to a much higher number of distinguishable sources with a higher accuracy. Compressive sensing based approach is applied for direction-of-arrival (DOA) estimation by vectorizing the fourth-order cumulant matrix of the array, assuming non-Gaussian impinging signals.

An effective sparse array extension method for maximizing the number of consecutive lags in the fourth-order difference co-array is proposed, leading to a novel enhanced sparse array structure based on co-prime arrays (CPAs) with significantly increased number of degrees of freedom (DOFs). One method to exploit the increased DOFs based on nonstationary signals is also proposed, with simulation results provided to demonstrate the effectiveness of the proposed structure.

In resource constrained wireless systems, achieving higher spectral efficiency (SE) and energy efficiency (EE), and greater rate fairness are conflicting objectives. Here, a general framework is presented to analyze the tradeoff among these three performance metrics in cooperative OFDMA systems with decode-and-forward relaying, where subcarrier pairing and allocation, relay selection, choice of transmission strategy, and power allocation are jointly considered. In our analytical framework, rate fairness is represented utilizing the α-fairness model, and the resource allocation problem is formulated as a multi-objective optimization problem. We then propose a cross-layer resource allocation algorithm across application and physical layers, and further devise a heuristic algorithm to tackle the computational complexity issue. The SE-EE tradeoff is characterized as a Pareto optimal set, and the efficiency and fairness tradeoff is investigated through the price of fairness. Simulations indicate that higher fairness results in a worse SE-EE tradeoff. It is also shown imposing fairness helps to reduce the outage probability. For a fixed number of relays, by increasing circuit power, the performance of SE-EE tradeoff is degraded. Interestingly, by increasing the number of relays, although the total circuit power is increased, the SE-EE tradeoff is not necessarily degraded. This is thanks to the extra degree of freedom provided in relay selection.

A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.

A simple scheme for direction of arrival (DOA) estimation of coherent targets in a multiple-input multiple-output (MIMO) radar is proposed. It is based on the idea of joint transmission and reception diversity smoothing. Compared to the existing transmission diversity smoothing (TDS) method, the major advantage of the new scheme is that more covariance matrices are available for averaging to decorrelate the coherent signals, leading to a better estimation result. Moreover, it is able to identify much more coherent targets than the TDS method when sparse arrays are used.

A robust minimum variance (MV) beamforming approach is proposed for improving the robustness of multiple‐input multiple‐output (MIMO) radar against the mismatches of the steering vector and the finite sample effects. In contrast to existing robust MV beamformers (RMVBs), the proposed RMVB utilises a specific structured model of virtual steering vector (also named transmit–receive steering vector) of MIMO radar rather than the commonly used unstructured model in phased‐array radar. The basic idea of the proposed RMVB is to estimate the desired transmit and receive steering vectors under two quadratic constraints. To solve this problem, an iterative algorithm is developed. Simulations are provided to confirm the effectiveness of the proposed method.

The attractiveness of majority-logic decoding is its simple implementation. However, it provides a modest amount of coding gain. A modified Massey's majority-logic decoding algorithm is proposed to improve error-correcting performance. With hard-decision and soft-decision, the modified algorithm is applied to decode the (30,14) shortened Reed-Muller (RM) code in TETRA as well as syndrome algorithm, Massey's majority-logic algorithm, and Rudolph's majority-logic algorithm. Simulation results show that, the proposed modified algorithm can provide more coding gain than the original Massey's algorithm, the modified scheme has the best performance of error correction among four hard-decision schemes, and the Rudolph's scheme outperforms the other three schemes in the case of soft-decision.