The IEEE 802.11bn draft amendment to the 802.11 Wi-Fi standard introduces Non-Primary Channel Access (NPCA) to enhance spectrum efficiency and mitigate congestion in dense wireless environments. NPCA allows stations to transmit over non-primary channels when the primary channel is occupied, improving overall spectrum utilization and reducing transmission delays. This paper presents a detailed evaluation of NPCA, focusing on its effectiveness in scenarios involving hidden nodes—one of the key challenges limiting its performance. Our research models NPCA based on IEEE contributions and proposals, implementing realistic network topologies to analyze both its strengths and weaknesses. The findings provide critical insights into NPCA’s practical deployment, highlighting its potential benefits and identifying areas requiring further optimization to ensure fairness and adaptability in real-world networks.

In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computation (AirComp). Log-FSK leverages non-linear signal processing to produce AirComp in the frequency domain, this is, the maximum frequency of the received signal corresponds to the sum of the individual transmitted frequencies. The demodulation procedure relies on the inverse Discrete Cosine Transform (DCT) and the extraction of the maximum frequency component. Log-FSK enables the computation of functions beyond the sum by incorporating nomographic function representation. Furthermore, unlike existing AirComp modulations, Log-FSK allows to compute several functions in a single transmission. We evaluate the capabilities of the scheme in an additive white Gaussian noise (AWGN) and flat-fading channels. To demonstrate its practicality, we present specific applications and experimental results showcasing the effectiveness of Log-FSK AirComp within linear Wireless Sensor Networks (WSN). Our numerical results show that Log-FSK outperform linear analog modulations in terms of MSE and power consumption.

Federated edge learning (FEEL) is a framework for training models in a distributed fashion using edge devices and a server that coordinates the learning process. In FEEL, edge devices periodically transmit model parameters to the server, which aggregates them to generate a global model. To reduce the burden of transmitting high-dimensional data by many edge devices, a broadband analog transmission scheme has been proposed. The devices transmit the parameters simultaneously using a linear analog modulation, which are aggregated by the superposition nature of the wireless medium. However, linear analog modulations incur in an excessive power consumption for edge devices and are not suitable for current digital wireless systems. To overcome this issue, in this paper we propose a digital frequency broadband aggregation. The scheme integrates a Multiple Frequency Shift Keying (MFSK) at the transmitters and a type-based multiple access (TBMA) at the receiver. Using concurrent transmission, the server can recover the type (i.e., a histogram) of the transmitted parameters and compute any aggregation function to generate a shared global model. We provide an extensive analysis of the communication scheme in an additive white Gaussian noise (AWGN) channel and compare it with linear analog modulations. Our experimental results show that the proposed scheme achieves no drop in performance up to -10 dB and outperforms the analog counterparts, while requiring 14 dB less in peak-to-average power ratio (PAPR) than linear analog modulations.

In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of$12\times $and$2\times $when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.