Microwave photonic (MWP) radar has the advantages of generating and processing wide bandwidth microwave signals, reconfigurability, high immunity to electromagnetic interference compared to microwave electronic radar. It has the potential to be used in applications such as intelligent autonomous and cyber-physical systems. Recent advances in microwave photonic technology led to the generation, fast processing, and control of broadband signals. Because of the advancements in photonic technologies, next-generation microwave photonic radar is becoming more prominent. This article reviews the most recent advancements and future directions in MWP radars. This review article overviews the different components of microwave photonic radar, different design challenges, and issues pertaining to it. We present a comparative study of different MWP radars on different applications. It also discusses possible future research directions of MWP radar.

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects on the network if they are not diagnosed and informed of the fault status to other nodes. Fault diagnosis is difficult when the nodes behave at fault sometimes and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set show that the new method provides better detection accuracy (DA) with the minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in a wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR, and 0% FPR if the probability of the data from faulty node exceeds 25%.

In a cognitive radio network, non-reconstruction-based wideband compressive spectrum sensing poses challenges under the non-Gaussian noise environment. The maximum correntropy criterion (MCC) is robust to impulsive noise whereas, the Parzen window Renyi entropy is a good choice for spectrum sensing in the presence of Gaussian noise at a low signal-to-noise ratio (SNR). However, the detection performance of these algorithms depends on the accuracy of measured noise variance, which is sensitive to impulse noise. In this letter, we improve the sensing performance of both the aforementioned algorithms in a non-Gaussian noise environment by modifying the kernel and threshold using robust statistics. The robust technique minimizes the influence of impulsive noise in the received signal. Finally, we carry out the simulation results to illustrate the superior performance of robust sensing algorithms under both Bernoulli’s distribution and symmetric $\alpha $ stable ( $\text{S}\alpha \text{S}$ ) distribution channel noise. The performance is compared with the non-robust counterpart for sensing multi-carrier Universal-Filtered Multi-Carrier (UFMC) signal.

Distributed wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and others. Energy efficiency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for WSN. This paper studies the problem of robust adaptive estimation in impulsive noise environment using robust cost function like Wilcox on norm and error saturation nonlinearity. The incremental cooperative scheme conventionally used in sensor network in which each node have local computing ability and share them with their predefined neighbors, is not robust to impulsive type of noise or outliers. In this paper the robust norm is introduced in incremental cooperative distributed network to estimate the desired parameters in presence of Gaussian contaminated impulsive noise.

In cellular Internet of Things, burst transmissions from millions of machine type communications (MTC) devices can result in channel congestion. The main bottleneck in such scenario is the inefficient random access channel (RACH) mechanism that is used to attach MTC devices to a base station (BS). To address this issue of congestion in RACH mechanism, 3GPP has proposed an extended access barring (3GPP-EAB) mechanism. However, several works indicate that the performance of the 3GPP-EAB mechanism can be further improved. In this work, a successive interference cancellation (SIC) based RACH mechanism is considered to significantly increase the RACH success rate and reduce congestion. In the proposed mechanism, the devices are allowed to transmit repeatedly for a finite number of times in a given radio frame, and thereafter, the success rate is improved by applying back-and-forth SIC at the BS. A Markov model of the proposed mechanism is presented with all transition and steady-state probabilities. Further, the probability of successful SIC for a given state of the Markov chain is derived. Through extensive numerical results, it is shown that the proposed mechanism significantly outperforms the state-of-the-art mechanisms in terms of the success rate and average access delay. Moreover, to maximize the success rate, the optimum number of devices to be entered in a radio frame is also calculated. Finally, we investigate the effect of imperfect SIC, increasing number of contending devices on the performance of the proposed mechanism.