Recently the use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has exploded in several domains, leading to potential security issues. As such, estimating the exact position of those eventual malicious drones has become of crucial interest. However, computing an accurate and precise geolocation of these drones, especially in outdoor environments, remains challenging. This paper focuses on outdoor 3-dimensional (3D) drone geolocation techniques based on Radio Frequency (RF) signals. We first present a RF-based 3D drone geolocation dataset, and then apply and compare various geolocation techniques, ranging from geometrical-based to machine learning-based methods. We further propose a new hybrid method blending the two above categories of geolocation techniques, that achieves an average 3D error of the order of 11.7 meters within a search volume of about 520×560×115 m3, significantly below the one achieved with geometrical-based techniques, and with a reduced computational complexity compared to the regular machine-learning based techniques.

In this paper, the energy efficiency of a two-user downlink NOMA system aided by several ambient backscatter devices is investigated. We analyze both the tradeoff and the ratio between achievable rates versus power consumption, assuming that the backscatter devices are in fully cooperative mode. In the case of two backscatter devices, we derive a closed-form solution in terms of the optimal reflection coefficients and power allocation policy by exploiting the properties of the energy-efficiency objective and the Pareto boundary of the feasible set. For more than two backscatter devices, the problem becomes difficult and our methodology cannot be extended easily. Nevertheless, we evaluate the performance of NOMA aided by several (up to four) backscatter devices via numerical simulations. Our numerical results show that the energy efficiency of the two-user NOMA system increases with the number of cooperative backscatter devices. Moreover, in the high noise regime, the relative efficiency gain increases with the number of backscatter devices reaching up to 370 % compared to conventional NOMA.

For finite blocklength polar codes, the minimum distance and the number of low weight codewords are essential to obtain good performance under successive cancellation list decoding with moderate and high list sizes. In this paper, we propose a code design method to decrease the number of low weight codewords for some information lengths with a very low computational complexity. In the proposed method, some information bits are encoded by several rows of the polar encoding matrix, i.e., each of the dynamic frozen bits is chosen the same as one of the preceding information bits. The dynamic frozen bit index set is determined by using the connection between the binary representation of the row indices and the number of common 1-bit positions of any given rows. The resulting design is shown to perform as well as polarization-adjusted-convolutional codes [9] under successive cancellation list decoding but with significant computational complexity savings. These findings pave the way for the use of polar codes in applications with stringent complexity and with low energy consumption constraints.

Reed Muller (RM) codes are known for their good minimum distance. One can use their structure to construct polar-like codes with good distance properties by choosing the information set as the rows of the polarization matrix with the highest Hamming weight, instead of the most reliable synthetic channels. However, the information length options of RM codes are quite limited due to their specific structure. In this work, we present sufficient conditions to increase the information length by at least one bit for some underlying RM codes and in order to obtain pre-transformed polar-like codes with the same minimum distance than lower rate codes. Moreover, our findings are combined with the method presented in [1] to further reduce the number of minimum weight codewords. Numerical results show that the designed codes perform close to the meta-converse bound at short blocklengths and better than the polarized adjusted convolutional polar codes with the same parameters.

Impulsive noise, where large amplitudes arise with a relatively high probability, arises in many communication systems including interference in Low Power Wide Area Networks. A challenge in coping with impulsive noise, particularly alpha-stable models, is that tractable expressions for the log-likelihood ratio (LLR) are not available, which has a large impact on soft-input decoding schemes, e.g., low-density parity-check (LDPC) packets. On the other hand, constraints on packet length also mean that pilot signals are not available resulting in non-trivial approximation and parameter estimation problems for the LLR. In this paper, a new unsupervised parameter estimation algorithm is proposed for LLR approximation. In terms of the frame error rate (FER), this algorithm is shown to significantly outperform existing unsupervised estimation methods for short LDPC packets (on the order of 500 symbols), with nearly the same performance as when the parameters are perfectly known. The performance is also compared with an upper bound on the information-theoretic limit for the FER, which suggests that in impulsive noise further improvements require the use of an alternative code structure other than LDPC.

In this paper, the energy efficiency of multi-user non orthogonal multiple access (NOMA) systems in the presence of a backscatter device is investigated. The energy efficiency maximization problem is formulated as a tradeoff between the sum rate and the total power consumption and shown to be non-convex. We then derive a closed-form expression of the optimal reflection coefficient. Remarkably, the obtained expression allows the reformulation of the optimization in terms of the power allocation policy into a convex optimization problem that has recently been solved in closed form. This overall solution can then be exploited to reduce the computational complexity of Dinkelbach's algorithm for maximizing the ratio sum rate vs. total power. Simulation results show that the presence of backscatter devices significantly improve the energy efficiency of NOMA systems and reach up to 450% relative gains compared to OMA.

This paper investigates the energy efficiency of a multi-user downlink non-orthogonal multiple access (NOMA) system. The energy-efficiency maximization representing the tradeoff between the sum rate versus the overall power consumption is formulated as a bi-criterion optimization problem; being convex, scalarization is suitable for its solution. Assuming that each individual user has a minimum quality of service constraint, we prove that the solution of the scalarized objective can be obtained in closed form. Hence, our solution characterizes the entire Pareto-optimal boundary of the rate vs. power tradeoffs. In the special case of maximizing the ratio sum rate vs. overall power, our solution reduces the complexity of the Dinkelbach procedure to a univariate bisection method. At last, our simulation results highlight the benefit of NOMA over OMA in terms of the rate vs. power optimal tradeoff.

This paper investigates a power allocation problem in an optimal and closed-form manner for a relay-aided cognitive network composed of one primary and secondary user/destination pair (e.g., a cellular link coexisting with a device-to-device link in a device-to-device enabled cellular communication setup) and one secondary full-duplex relay. By exploiting the monotonic properties of the objective function and the geometry of the feasible set, we provide the optimal and closed-form power allocation policy assuming the relay performs either Decode-and-Forward (DF) or Compress-and-Forward (CF). We then conjecture that if the secondary direct link is missing, then DF always outperforms CF, irrespective from the system parameters. This conjecture is sustained by numerous numerical simulations.

The covert capacity is characterized for a non-coherent fast Rayleigh-fading wireless channel, in which a legitimate user wishes to communicate reliably with a legitimate receiver while escaping detection from a warden. It is shown that the covert capacity is achieved with an amplitude-constrained input distribution that consists of a finite number of mass points including one at zero and numerically tractable bounds are provided. It is also conjectured that distributions with two mass points in fixed locations are optimal.

In impulsive noise, the inputs of the belief propagation decoder can be complex to compute or even impossible when the noise distribution is not known. We propose a simple approximation of the log-likelihood ratio that maps the channel output to the input of the error correcting decoder, for instance, LDPC decoders. This approximation is designed for additive impulsive noise channels, nevertheless, it is not computationally demanding and easy to be implemented. It requires the estimation of three parameters and we propose an efficient way to do it. Moreover, in terms of performance, our solution is barely discernible from the optimal receiver which is computationally prohibitive.

Robust communication is necessary for many wireless applications. Making a decision at the receiver requires an evaluation of the likelihood. However, in impulsive noise, the traditional Gaussian-based receiver exhibits a very significant performance loss. This paper proposes to approximate the likelihood ratio in a binary transmission with a function adapted to impulsive noise conditions but also efficient when noise is purely Gaussian. We introduce a blind estimation of the two parameters defining the approximation and evaluate its performance when used as the inputs of the belief propagation decoder. Our proposal allows us not only to achieve performance close to the optimal decoding but also to have a simple implementation and to adapt to different environment, impulsive or not, independently of the underlying statistical noise model, without the need of a training sequence.

This paper focuses on the full-duplex Gaussian two-way diamond relay channel (TWDRC), where two users wish to exchange their messages with the help of two relays, when one relay performs a lattice-based Compress-and-Forward (CF) scheme. The other relay performs either Decode-and-Forward (DF), Amplify-and-Forward (AF) or a lattice-based CF scheme. We start by proposing a novel lattice-based CF/CF scheme, where both relays perform CF. As opposed to the single relay case, two main challenges arise: the quantization rate region is constrained by two Multiple Access Channel (MAC) rate regions, and each user has to combine two noisy observations to decode the sent message. These challenges are tackled by exploiting MAC techniques and Maximum Ratio Combining methods, which are used to combine the two noisy observations. We then characterize a general achievable rate region when one relay sends a version of both users' messages (eventually noisy), which encompasses both AF and DF, and the other relay performs CF and derive the corresponding achievable rate regions. Finally, the three proposed relaying schemes are compared via numerical evaluations. Clearly, the position of both relays has an impact on the best relaying strategy.

Fast varying active transmitter sets are a key feature of wireless communication networks with very short transmissions arising in machine-to-machine communications. A consequence is that the interference is dynamic, leading to non-Gaussian statistics. In this paper, we study the behavior of large scale communication networks in the presence of isotropic α-stable interference, which forms a model for dynamic interference. We first characterize the achievable rate of each link by considering a non-Gaussian input distribution, which is shown to outperform a Gaussian input. Moreover, we analyze the area spectral efficiency, which is the total rate per square meter. Our analysis suggests that analogously to the common model of slowly varying active transmitter sets, dense networks maximize the area spectral efficiency.

This paper investigates two classes of relay channels, the Gaussian relay channel and the Gaussian two-way relay channel, when additive noises at the relay and destination(s) are correlated. Lattice codes are used to achieve the rate region for Compress-and-Forward (relay channel) and Compress/Decode-and-Forward (two-way relay channel). Numerical calculations show that there exist particular values of the correlation co-efficient such that the gap between the Cut-Set Bound (CSB) and the proposed schemes is minimal.

This paper studies the extension of the multiway relay channel model with restricted encoders (introduced by Gündüz et al.) by adding unit-gain intra-cluster links. In this model, multiple clusters of users communicate with the help of one relay and the users within a cluster wish to exchange messages among themselves. We obtain achievable rates and gaps to the cut-set bound as a function of the number of users and the cluster-to-relay gain g.

This paper presents a new iterative decoding algorithm for the source coding with coded side information problem. Side information (SI) is compressed to an index by a many-to-one (quantization) function. Instead of using the reconstruction corresponding to the quantization index as a single representative SI word to aid the main decoder, one can modify it by projecting an intermediate estimate of the source word onto the Voronoi cell associated to the SI index. The hope is that the projection brings the representative SI word closer to the source word, and thus accelerates iterative decoding. Simulations using LDPC syndrome coding in the main branch and trellis-coded quantization in the SI branch show that for a fixed number of decoder iterations, this method indeed increases the number of correctly decoded source words. In fact, the decoding threshold is shifted, which may be attributed to a partial compensation of the suboptimality of the quantizer.