Side-channel attacks consist of retrieving internal data from a victim system by analyzing its leakage, which usually requires proximity to the victim in the range of a few millimetres. Screaming channels are EM side channels transmitted at a distance of a few meters. They appear on mixed-signal devices integrating an RF module on the same silicon die as the digital part. Consequently, the side channels are modulated by legitimate RF signal carriers and appear at the harmonics of the digital clock frequency. While initial works have only considered collecting leakage at these harmonics, our work has demonstrated that the leakage is also present at frequencies other than these harmonics. This result significantly increases the number of available frequencies to perform a screaming-channel attack, which can be convenient in an environment where multiple harmonics are polluted. This paper studies how this diversity of frequencies carrying leakage can be used to improve attack performance. We first study how to combine multiple frequencies. Second, we demonstrate that frequency combination can improve attack performance and evaluate this improvement according to the performance of the combined frequencies. Finally, we demonstrate the interest of frequency combination in attacks at 15 and, for the first time, at 30 meters in an RF-polluted environment. One last important observation is that this frequency combination divides by at least 2 (and up to 3.76) the number of traces needed to reach a given attack performance.

This work investigates Generalized Receiver Spatial Modulation (GRSM) in a massive MIMO downlink scenario over millimetre wave channels. While GRSM enhances spectral efficiency (SE) and reduces power consumption, indexing additional bits using the spatial dimension increases the vulnerability to detection errors. These errors primarily stem from thresholddependent spatial detection. We propose a novel predefined threshold computation method minimizing spatial detection errors, rigorously validated through the Maximum A Posteriori (MAP) criterion. Furthermore, we derive an analytical Average Bit Error Probability (ABEP) expression tailored for energy detection, exploiting inherent constellation energy distributions. The analytical derivation was validated via link-level simulations under two scenarios: (i) perfect spatial detection, and (ii) practical spatial detection. The results show spatial errors dominating overall performance, shifted from theoretical values by practical spatial detection using multiple thresholds for different energy levels in 16QAM. Crucially, an inverted error trend revealed between GRSM-4QAM and GRSM-16QAM, highlighting a tradeoff between error resilience, complexity, and energy efficiency.

The standard multiple-input multiple-output (MIMO) detectors exploit the available information to resolve the detection problem. Alternative algorithms, such as bioinspired or geometrical detectors, mix exploitation with exploration to bypass local minima and enhance the results. This paper examines the benefits of adding exploration to the traditional tree-based detectors. For this purpose, a new interpretation of the bio-inspired detector based on the firefly algorithm (FA) is proposed. It is studied in a tree search paradigm and extended to soft-outputs. The findings suggest that the addition of a stochastic exploration to tree-based detectors significantly improves performance with a small computational overhead.

Non-Orthogonal Multiple Access (NOMA) is one of the promising techniques to ensure very high spectral efficiency in 5G mobile communications and beyond. In contrast to the orthogonal multiple access (OMA) technique, the NOMA shows outstanding performances in terms of throughput, user fairness, low latency and compatibility with the current and future communication systems. In this paper, we analyze the capacity region in NOMA system and the limited number of multiplexed users under a given power allocation vector (i.e. symmetric/asymmetric channel). In addition, we compare the downlink capacity of the OMA/NOMA systems. Moreover, we have investigated the effect of large constellation order on the power allocation and bit error rate (BER). Comparisons between the coded and uncoded schemes are also presented.

This letter presents a rigorous demonstration of the convergence of the Newton fractional delay filter to the ideal fractional delay filter. The Newton structure is a very efficient implementation of Lagrange interpolation using a variable fractional delay filter structure. Through the developed demonstration, a new approach is proposed to define the ideal fractional delay filter as the limit of any digital filter implementing on Lagrange interpolation. This letter also proves that the Z-transform expression of the ideal fractional delay has a fully defined frequency response on the unit circle.

In this paper, we present an analytical lower bound on the ergodic capacity of optical multiple-input multiple-output (MIMO) channels. It turns out that the optical MIMO channel matrix which couples the mtinputs (modes/cores) into mroutputs (modes/cores) can be modeled as a sub-matrix of a m × m Haar-distributed unitary matrix where m > mt, mr. Using the fact that the probability density of the eigenvalues of a random matrix from unitary ensemble can be expressed in terms of the Christoffel-Darboux kernel. We provide a new analytical expression of the ergodic capacity as function of signal-to-noise ratio (SNR). Moreover, we derive a closed-form lower-bound expression to the ergodic capacity. In addition, we also derive an approximation to the ergodic capacity in low-SNR regimes. Finally, we present numerical results supporting the expressions derived.

In this study, the authors consider the standard condition number (SCN) detector for a cognitive radio with finite number of cooperative sensors. They derive an exact nested form of the distribution of the SCN for the central uncorrelated, non‐central uncorrelated and central semi‐correlated Wishart matrices under and hypotheses. Due to the complexity of these expressions, the authors approximate the distribution of the SCN by the generalised extreme value distribution using moment matching. They derive the exact form of the p th moment of the SCN for these cases. Consequently, the performance probabilities are approximated and a simple decision threshold formula is provided. In addition, a similar approximation for the detection probability is provided using non‐central/central approximation. They show that the proposed analytical approximations provide high accuracy using Monte‐Carlo simulations.

Spectrum sensing is a fundamental problem in cognitive radio systems. Its main objective is to reliably detect signals from licensed primary users to avoid harmful interference. As a first step toward building a large-scale cognitive radio network testbed, we propose to investigate experimentally the performance of three blind spectrum sensing algorithms. Using random matrix theory to the covariance matrix of signals received at the secondary users, the first two sensing algorithms base their decision statistics on the maximum to minimum eigenvalue ratio and the sum of the eigenvalues to minimum eigenvalue ratio, respectively. However, the third algorithm is based on cyclostationary feature detection and it uses the symmetry property of cyclic autocorrelation function as a decision policy. These spectrum sensing algorithms are blind in the sense that no knowledge of the received signals is available. Moreover, they are robust against noise uncertainty. In this paper, we implement spectrum sensing in real environment and the performance of these three algorithms is conducted using the GNU-Radio framework and the universal software radio peripheral (USRP) platforms. The results of the evaluation reveal that cyclostationary feature detector is effective in finite sample-size settings, and the gain in terms of the SNR with respect to eigenvalues-based detectors to achieve Pfa (probability of false alarm) = 0.08 is at least 4 dB.

Spectrum sensing is the very task upon which the entire operation of Cognitive Radio rests. In this paper, we propose a spectrum sensing technique based on the estimates of the spectrum of a multiband signal obtained from its non-uniform compressed multicoset samples. We show that our proposed spectrum sensing method provides accurate results using less data samples. We discuss in detail the effect of false detections on the quality of the reconstructed signal obtained from non-uniform multicoset samples.

The maximum likelihood (ML) detection is the process to find the nearest lattice point to a given one in an N-dimensional search space. The ML problem is well known to be NP-hard. In this paper, we propose a near-maximum likelihood detection algorithm based on an intensification strategy over an initially efficient and uniformly distributed subset. This subset is given by a diversification step based on powerful uniformly distributed codes. The proposed algorithm has three characteristics that make it attractive for several practical wireless communication systems. First, the simulated bit error rate performance shows that this algorithm provides a good approximation to the ML detector. Second, it has a constant polynomial-time computational complexity. Finally, the inherent parallel structure of this algorithm leads to a suitable hardware implementation.

In this paper, we propose a novel approach for multiple antenna spectrum sensing based on compressed sensing. Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different Directions Of Arrival (DOA). Given multiple snapshots over a small time period, multiple measurement vectors are available and a joint sparse recovery is performed to estimate the common sparsity profile over the angular domain. In this estimation process, we employ the regularized M-FOCUSS algorithm [1], which is the noisy multiple snapshot extension of the iterative weighted minimumnorm algorithm, called FOCUSS. The contribution of this paper is to take advantage of the sparse primary user DOA estimation within the detection framework of multiple antenna spectrum sensing. In this scope, an accurate sparse reconstruction is not required and a coarse estimation using a reduced number of snapshots is sufficient to decide about the number of present primary users reflected by the angular sparsity order of the received signal. A simulation study shows significant constant false alarm rate performance gain of the proposed approach compared to the conventional maximum to minimum eigenvalue detector especially when the number of PUs increases.

Based on the sparse property of the cyclic autocorrelation in the cyclic frequencies domain, this paper proposes a new blind spectrum sensing method which uses the compressed sensing technique in order to detect free bands in the radio spectrum. This new sensing method that presents a relative low complexity has the particularity to perform blind and robust detection with only few samples (short observation time) and without any knowledge about the cyclic frequency of the signal, in contrary to cyclostationary detection methods that are not robust when the sample size is small and might need some information about the signal in order to detect. ROC curves obtained by simulation show the superiority of the new proposed technique over cyclostationary detection under the same conditions, particularly the same observation time.

This paper presents the ANR project IDROMel, which aims at developing reconfigurable SDR (software defined radio) and cognitive radio (CR) equipments. IDROMel is a 3 years project that started in 2005 and finishes in 2009. The main objective of IDROMel is to define, develop and validate a powerful SDR and CR platform combining very last technology progresses. The platform includes software parts (reconfigurable protocol stacks) and hardware parts (a base band board and a radio frequency front end, RF). Both parts are presented in this paper.

It has been widely shown that the sphere decoding can be used to find the maximum likelihood (ML) solution with an expected complexity that is roughly cubic in the dimensions of the problem. However, the computational complexity becomes prohibitive if the signal-to-noise ratio is too low and/or if the dimension of the problem is too large. That is why another technique denoted as fixed-complexity sphere decoder (FSD) is an interesting approach. This algorithm needs a preprocessing step, and in this paper the QR-decomposition-based preprocessing technique, which is not inconsequential, will be studied. Two different techniques are exposed, including the classical Gram Schmidt orthonormalization process. Their computational complexities and their impacts on the FSD computational complexity are studied. In the LTE context, the overall computational complexities of the two detection techniques are quantified and are shown to be dependent on the constellation size.

This letter proposes a quasi optimum maximum likelihood detection technique based on Geometrical Diversification and Greedy Intensification (GDGI). The presented detector scheme is shown to achieve almost optimal performance for all signal-to-noise ratio (SNR) values and a cubic computation complexity in the problem dimension. It possesses a regular structure well suited for hardware implementation. Simulation results show that for a system with a high dimension of n = 60, the loss is approximately 0.35 dB at BER=10-5compared to an optimal decoding.

An efficient sub-optimal algorithm, called HIS (hyperplane intersection and selection) detection algorithm, is proposed to solve the problem of joint detection of K users in an MC-CDMA system. Compared to existing solutions, the proposed algorithm has three characteristics very attractive for practical. systems. Firstly, it has nearly optimal performance. Secondly, it has a low computational complexity - O(K/sup 2/) multiplications and O(K/sup 3/) additions. Third, the algorithm has an inherent parallelism. To our knowledge, the HIS algorithm is not just an add-on to an existing algorithm, but rather a new decoding technique based on a singular value decomposition of the channel matrix, H. After giving the equation of the MC-CDMA multi-user detection problem, the HIS algorithm is described. Its performance is compared to known existing algorithms (ZF, MMSE, PIC and sphere decoding). For a BER as low as 10/sup -4/, the HIS algorithm introduces only 0.2 dB degradation compared to the optimal sphere decoding algorithm for K=16 users against 3.8 dB for the PIC algorithm with two MMSE stages.

The re-use of complex digital signal processing (DSP) coprocessors can be improved using IP cores described at a high abstraction level. System integration, which is a major step in SoC design, requires taking into account communication and timing constraints to design and integrate IP. In this paper, we describe an IP design approach that relies on three main phases: constraints modeling, IP constraints analysis steps for feasibility checking, and synthesis. Based on a generic architecture, the presented method provides automatic generation of IP cores designed under integration constraints. We show the effectiveness of our approach in a case study of a maximum a posteriori (MAP) algorithm for a turbo decoder.