We present an analysis on multi-user pairwise error probability (PEP) performance of non-orthogonal multiple access (NOMA)-aided power line communication (PLC) systems, under heavy-tailed noise. In contrast to the typical assumption in the literature, we consider the successive interference cancellation procedure in NOMA to be imperfect. We model the heavy-tailed noise as generalized Gaussian (GG) distribution with a heavy-tail parameter, and discuss the impact of this parameter on the PEP performance. We validate our analysis using Monte Carlo simulations, and disucss the impact of the lognormal fading parameters in PEP. Further, we numerically show that the diversity order on PEP is equal to the user order for larger values of the heavy-tail parameter, which is similar to that observed in an additive white Gaussian noise scenario.

Efficient target localization is a requirement and challenge in various applications such as autonomous vehicle systems, defence, and indoor positioning. Radars and lidars are used in such applications where the range-doppler matrix representations are common and found to be very useful. In this paper, we address the problem of target localization from a range-doppler two-dimensional (2D) matrix in latency-sensitive radar systems which support hardware acceleration. The proposed algorithms consist of three key steps, namely (a) initial background noise filtering and binarization, (b) artifact filtering, and (c) 2D target localization. We use K-means clustering and morphology to filter background noise and artifacts respectively. To implement the 2D target localization effectively, we propose a novel two-layer connected components algorithm, which reduces the computations and hence latency in target localization compared to the conventional connected components algorithm up to 64.37%. Simulation results are provided to demonstrate the performance of the algorithm in the presence of additive noise, which is compared with existing methods.

We consider a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. We propose a scheme to exploit the multiple antenna structure of the system to achieve a lower error-rate performance than conventional ABC systems. Furthermore, we present a novel low complexity energy-based maximum likelihood (EML) GSSK detector, which does not require the perfect knowledge of the ambient source’s signal, unlike the conventional ABC receivers. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression as the number of antennas of the reader becomes large. Finally, we derive a simple, asymptotic PEP at the reader when the noise variance approaches zero, i.e., under the large signal-to-interference ratio regime. We validate our analysis through Monte Carlo simulations and show a small performance loss due to the approximations.

We propose two transfer learning-based deep neural network architectures for 2D-to-3D single-view image reconstruction, with an emphasis on low computational resources for training and high reconstruction performance. The proposed models, namely AE-Dense and 3D-SkipNet use DenseNet and ResNet architectures in the encoder, with additional skip connections. Through extensive experimental study on the 3D ShapeNets database, we show that the proposed models outperform state-of-the-art models, namely Pix2Vox and 3D-R2N2, in terms of intersection over union (IoU) metric. In particular, the AE-Dense offers the highest IoU, while the 3D-SkipNet yields a significant reduction in memory and training time, compared to Pix2Vox and 3D-R2N2.

In this paper, we analyze the performance of relay-assisted (RA) non-orthogonal multiple access (NOMA) power line communications (PLC) systems. For a setup of three PLC modems, namely, a source (S), relay (R), and a destination (D), we present closed-form expressions for the outage probabilities at R and D. We consider maximal ratio combining at D and decode-and-forward protocol at R. Moreover, we formulate an optimization problem and present a closed-form solution for the optimal power allocation coefficients at S. Furthermore, we show that numerical and Monte Carlo simulation results corroborate the derived analytic expressions.

We propose a novel modified Mel-discrete cosine transform (MMD) filter bank structure, which restricts the overlap of each filter response to its immediate neighbor. In contrast to the well-known triangular filters employed in the extraction of the Mel-frequency cepstral coefficients (MFCC), the proposed filter structure has a smoother response and offers discrete cosine transformation and Mel-scale filtering in a single operation. It is known that the choice of MFCC as the only feature for voice activity detection (VAD) does not yield substantial improvements in the performance. Even with the long-term approach, we observe a not so encouraging VAD performance when MFCC features are employed. However, other long-term based VAD algorithms - without MFCC - are known to provide a substantial improvement in the performance under low SNR with time-varying statistics of speech and/or noise. In this work, we show that by employing the MMD followed by the long-term differential entropy of voice signal for VAD provides significant improvements in detection accuracy when compared with the other well-known long-term algorithms. Thus, this study opens up the possible benefits of the proposed MMD filter bank for other speech processing applications.

We propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. The proposed framework considers opportunistic spectrum sensing integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, average energy consumption and energy efficiency in the considered set up. In addition, we formulate an optimization problem to maximize the energy efficiency of the CR system operating in mixed ABC- and HTT-modes, subject to primary interference and energy harvesting constraints. Next, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, and the optimal degree of tradeoff between the CR system operating in the ABC- and HTT-modes. We present extensive numerical results to corroborate our analysis and to demonstrate the performance gain of the proposed model in terms of energy efficiency.

We investigate the energy efficiency of a conventional collaborative compressed sensing (CCCS) scheme in cognitive radio networks. In particular, we derive expressions for the throughput, energy consumption and energy efficiency, and analyze the trade-off between the achievable throughput and the energy consumption of the underlying CCCS scheme. Furthermore, we formulate a multiple variable non-convex optimization problem to determine the optimum compression level that maximizes the energy efficiency, subject to interference constraints. We propose a sub-optimal solution based on tight approximations to simplify the aforementioned optimization problem, and further demonstrate that the energy efficiency achieved by the CCCS scheme is higher than that of conventional collaborative sensing scheme, under the same predefined conditions. It is further shown that the increase in the energy efficiency of CCCS scheme is due to the considerable decrease in the energy consumption, which is particularly noticeable with a large number of sensors.

In this work, we consider the performance analysis of maximum eigenvalue-based detector (MED) for spectrum sensing in cognitive radios, under α-κ-μ and α-η-μ fading channels. The MED is known to be an asymptotically optimal test when the primary signal is unknown. Under the described fading conditions, the average probability of detection can be calculated over the probability density function (PDF) of the received SNR. In general, finding the expressions for PDF of SNR under both α-κ-μ and α-η-μ fading are difficult. However, experimental results reported in literature reveal that both these fading models accurately fit the fading model in several scenarios in an around some specific values of the underlying parameters. Motivated by this, we propose several approximations to the PDF of the received SNR under these fading models and validate our expressions through Monte Carlo simulations. Also, a detailed performance simulation study is discussed.

We study the energy efficiency of superior selective reporting (SSR)-based scheme for spectrum sensing in cognitive radio networks. We first derive the achievable throughput, energy consumption and energy efficiency (EE) of the SSR scheme. Later, we propose the maximization of EE for the SSR scheme as a multiple variable non-convex optimization problem and provide approximations to reduce it to a quasi-convex optimization problem. We highlight that the errors due to these approximations are negligible. Through numerical results, we show that the SSR scheme outperforms the conventional schemes that employ the OR and AND fusion rules, in terms of energy efficiency.

In this work, spectrum sensing for cognitive radios is considered in the presence of Primary Users (PU) using frequency-hopping communication over multiple frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses.

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.