Nowadays, the development of efficient communication system is necessary for future networks. Compressive sensing was proposed as a technique to save storage and energy by compressing signals using simple linear transformations. Although compressed signals can be perfectly recovered, the complexity of the reconstruction operation is high. However, there are applications where compressive signals are processed directly in the compressed domain, with spectrum sensing being an example. Several works apply classical statistical detectors for extracting information from compressed signals, but an emerging concept, denoted as compressive learning, uses machine learning algorithms to extract information from compressed signals and it has promising applications in telecommunications. Compressive learning is being pointed-out as an important technique for future networks, where detecting patterns from a large amount of data is a key feature for new applications. In this paper, we investigate the compressive learning approach applied to spectrum sensing for cognitive radios. We assume that the information about the channel occupancy is collected by spatially distributed sensors and then concentrated in a gateway. The gateway compresses the signals and employs orthogonal frequency division multiplexing to transmit the data to the fusion center, responsible for the final decision about the channel status. We propose a detector based on neural networks to recover information about the occupancy of the channel from the compressed signal and compare it with the optimum maximum likelihood detector, assuming perfect and imperfect channel state information. Results demonstrate that both detectors achieve comparable performance, whereas our proposal has lower complexity.

This paper addresses the performance of an efficient fusion scheme for cooperative spectrum sensing in the context of cognitive radio systems. The secondary users' decisions are transmitted to the fusion center at the same time and using the same carrier frequency, thus saving bandwidth and time resources of the report channel. The report channel state information and the receiver thermal noise variance are the main parameters used by the decision rule at the fusion center to determine whether the primary signal is present or absent. The global spectrum sensing performance is addressed in this paper when the fusion center receiver is subjected to impulsive noise and uncertainty in the estimation of the above parameters. It is demonstrated that the receiver is quite robust against noise variance uncertainty and impulsive noise, whereas its performance may be severely degraded due to channel state information uncertainty.

The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas increases. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the algorithm of the probability data association (PDA) detector is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector, yet presenting no severe penalties in performance in terms of bit error rate (BER).

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This paper presents an unified analysis of multipleinput multiple-output (MIMO) detectors which aims to shed light on the compromise between complexity and symbol error rate (SER) performance, showing the conditions in which each detector is more interesting. To demonstrate this unified approach, five detectors with different levels of complexity and performance are evaluated under a large-scale MIMO scenario.