This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm based on Bidirectional Long Short-Term Memory cells (a specific type of Recurrent Neural Network) and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. In addition, the study proposes a novel master-centric methodology for managing the AP-UE association process, which helps improve scalability not only with respect to the number of UEs, but also to that of APs. Furthermore, a variant of the proposed AP-UE algorithm ensures robustness against pilot contamination effects, a critical issue arising from pilot reuse in channel estimation. Extensive numerical results validate the effectiveness and adaptability of the proposed methods, demonstrating clear advantages over widely used heuristic alternatives.

Iterative detection-and-decoding for multi-input multi-output (MIMO) communication systems require a soft-input soft-output (SISO) detection algorithm, which, in the optimal formulation, is well known to be exponentially complex in the number of transmitting antennas. This paper presents a novel SISO detector for MIMO systems, named SISO king decoder. It is a tree-search branch-and-bound algorithm, which exploits the properties of the channel matrix and the a-priori information on the transmitted bits to reduce the overall computational complexity. The proposed algorithm is compared with the SISO single tree-search sphere decoder [1]. Simulation results are provided to show the complexity reduction without relevant performance loss in terms of bit-error rate.

In this paper the choice of the Bernoulli distribution as biased distribution for importance sampling (IS) Monte-Carlo (MC) simulation of linear block codes over binary symmetric channels (BSCs) is studied. Based on the analytical derivation of the optimal IS Bernoulli distribution, with explicit calculation of the variance of the corresponding IS estimator, two novel algorithms for fast-simulation of linear block codes are proposed. For sufficiently high signal-to-noise ratios (SNRs) one of the proposed algorithm is SNR-invariant, i.e. the IS estimator does not depend on the cross-over probability of the channel. Also, the proposed algorithms are shown to be suitable for the estimation of the error-correcting capability of the code and the decoder. Finally, the effectiveness of the algorithms is confirmed through simulation results in comparison to standard Monte Carlo method.

In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

In this paper we present a theoretical performance analysis of the maximum ratio combining (MRC) rule for channel-aware decision fusion over multiple-input multiple-output (MIMO) channels for (conditionally) dependent and independent local decisions. The system probabilities of false alarm and detection conditioned on the channel realization are derived in closed form and an approximated threshold choice is given. Furthermore, the channel-averaged (CA) performances are evaluated in terms of the CA system probabilities of false alarm and detection and the area under the receiver operating characteristic (ROC) through the closed form of the conditional moment generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC) quadrature rules. Furthermore, we derive the deflection coefficients in closed form, which are used for sensor threshold design. Finally, all the results are confirmed through Monte Carlo simulations.

This paper deals with spectrum sensing for cognitive radio scenarios where the decision fusion center (DFC) exploits array processing. More specifically, we explore the impact of user cooperation and orthogonal transmissions among secondary users (SUs) on the reporting channel. To this aim four protocols are considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and cooperative. The DFC employs maximum ratio combining (MRC) rule and performance are evaluated in terms of complementary receiver operating characteristic (CROC). Analytical results, coupled with Monte Carlo simulations, are presented.

We study channel-aware binary-decision fusion over a shared Rayleigh flat-fading channel with multiple antennas at the Decision Fusion Center (DFC). We present the optimal rule and derive sub-optimal fusion rules, as alternatives with improved numerical stability, reduced complexity and lower system knowledge required. The set of rules is derived following both "Decode-and-Fuse" and "Decode-then-Fuse" approaches. Simulation results for performances are presented both under Neyman-Pearson and Bayesian frameworks. The effect of multiple antennas at the DFC for the presented rules is analyzed, showing corresponding benefits and limitations. Also, the effect on performances as a function of the number of sensors is studied under a total power constraint.

A new multiple access scheme based on energy discrimination is proposed. Such a scheme is based on the differences in the average power received by each user. It is particularly useful when no extra resources are available by means of existing schemes; moreover, it is useful when the system complexity must be kept very limited.

The probabilistic association algorithm (PDA) is proposed as a quasi-optimal solution for synchronous overloaded CDMA detection. Overloaded CDMA is useful in applications in which the band is a limited resource and more users are to be conveyed on the same channel. The PDA algorithm, already successfully applied to underloaded CDMA, is extended to the more difficult overloaded CDMA problem. PDA uses dynamic soft updates for the a posteriori probabilities and it is derived under the Gaussian assumption for the superposition of multiuser interference and Gaussian noise. A discussion on the separability of the two-class problem suggests how a dynamic PDA (D-PDA) may solve the problem more effectively in comparison to a fixed-order PDA (F-PDA). Performance results are reported in this paper and show that D-PDA can provide good decoding with limited computational complexity with the bit error rate going to zero as the signal-to-noise ratio increases.

In this letter, we derive a gradient-based algorithm for computing the optimal transform when coefficients are transmitted over an erasure channel whose statistics are known. The discrete transform introduces correlation among the coefficients with consequent performance improvement against losses. Simulations show appreciable improvements over standard schemes and also good robustness when loss probabilities are only roughly estimated.