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Gathering useful and trustworthy wireless data using crowdsourcing to train/validate machine learning (ML) algorithms can be difficult due to two factors: 1) correctness and reliability of the gathered data from various independent wireless access points (APs) can be unknown, and 2) designing a metric that can capture the value of the collected data for the considered model can be a challenge. To address the challenge of reliable data collection, we propose a game-theoretic crowdsourcing-based wireless data collection mechanism that can be used to reliably collect the data. We also propose a metric that can capture the value of the collected data for the considered model. Our proposed method provides protection against selfish deviations and unlike other game-theoretic works does not make an assumption that the actions of the crowdsourcing agent APs are known to the crowdsourcing entity. We consider a technique that can be used to infer the actions of agent APs and propose that the participants utilize the n-round Win-stay lose shift (WSLS) strategy. In our work, we compare the performance of the proposed strategy against various other game-theoretic strategies, such as always high, always low, WSLS with probability p, and tit for tat. In our performance evaluation, we utilize both real and synthetic wireless channel utilization data. Our results show that the n-round WSLS strategy outperforms the other game-theoretic strategies.

Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for $M$ -ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume $M=\infty $ to avoid the brute-force search. Due to the assumption of $M$ , the proposed models are independent of $M$ and can handle adaptive modulation where $M$ can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model.

Deep learning based channel estimation techniques have recently found an overwhelming interest owing to data-driven learning-based adaptability compared to conventional estimation techniques which rely on model-based approach. This paper exploits the availability of low cost software defined radio (SDR) devices to implement and test over-the-air (OTA) deep learning driven channel estimation solutions in realistic settings for 5G and beyond wireless communications. We have designed and implemented a ZYNQ SDR-based deep learning driven channel estimation platform which utilises real-world 5G new radio (NR) signals to develop and test the performance of a deep learning solution for wireless channel estimators. To this end, we have considered the time-frequency response of an OTA communication channel for single-input-single-output (SISO) and single-input-multiple-output (SIMO) networks under various line-of-sight and non-line-of-sight scenarios as a two-dimensional image. We have utilised convolutional deep learning technique for channel estimation. The main purpose is to use OTA samples obtained via the SDR platform to determine the unknown values of the channel response using known values at the pilot locations and evaluate performance compared to conventional technique. Our results show that the performance of the deep learning channel estimator using OTA data is as good as that of conventional methods with the flexibility to adapt based on data analytics of the time-varying nature of the channel.

Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands. We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under $2N $ iterations where $N $ is the number of network access points.

Wireless traffic usage forecasting methods can help to facilitate proactive resource allocation solutions in cloud managed wireless networks. In this paper, we present temporal and spatial analysis of network traffic using real traffic data of an enterprise network comprising 470 access points (APs). We classify and separate APs into different groups according to their traffic usage patterns. We study various statistical properties of traffic data, such as auto-correlations and cross-correlations within and across different groups of APs. Our analysis shows that the group of APs with high traffic utilization have strong seasonality patterns. However, there are also APs with no such seasonal patterns. We also study the relation between number of connected users and traffic generated, and show that more connected users do not always mean more traffic data, and vice versa. We use Holt-Winters, seasonal auto-regressive integrated moving average (SARIMA), long short-term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN) methods for forecasting traffic usage. Our results show that there is no single universal best method that can forecast traffic usage of every AP in an enterprise wireless network. The combined models such as CNN-LSTM and CNN-GRU are also used for spatio-temporal forecasting of a single AP traffic usage. The results show that considering spatial dependencies of neighboring APs can improve the forecasting performance of a single AP if it has significant spatial correlations.

A wide range of new ultra reliable low latency communication (URLLC) applications in next generation (NG) wireless systems demand real-time radio frequency (RF) data analytics of channel utilization (CU) that can help in making proactive resource allocation decisions. However, such real-time RF data analytics require processing of tens of millions of in-phase and quadrature (IQ) samples per second and sending huge quantities of samples to a resource allocating entity is not practical. We present design and implementation of an RF data analytics system which utilizes field-programmable gate arrays (FPGAs) at the network edge to process real-time streaming IQ samples from RF transceiver. FPGAs process millions of samples per second and output low-overhead descriptive statistics of wireless CU, such as mean CU values, maximum CU values, and entire histograms to obtain probability distribution of CU values, to a resource controller server where a quantile estimation based technique is used to detect congestion in CU in real-time. The FPGA-based modules are implemented on Xilinx's Zynq-7000 devices mounted with RF transceivers. We evaluate the performance of the implemented analytics system using extensive measurements, testing, and statistical analyses that are performed in both laboratory and over-the-air environments.

Hardware accelerated modules that can continuously measure/analyze resource (frequency channels, power, etc.) utilization in real-time can help in achieving efficient network control, and configuration in cloud managed wireless networks. As utilization of various network resources over time often exhibits broad and skewed distribution, estimating quantiles of metrics to characterize their distribution is more useful than typical approaches that tend to focus on measuring average values only. In this paper, we describe the development of a real-time quantile-based resource utilization estimator module for wireless networks. The intensive processing tasks run on the FPGA, while the command and control runs on an embedded ARM processor. The module is implemented by using high level synthesis (HLS) on a Xilinx's Zynq-7000 series all programmable system on chip board. We test the performance of the implemented quantile estimator module, and as an example, we focus on forecasting congestion with real frequency channel utilization data. We compare the results from the implemented module against the results from a theoretical quantile estimator. We show that with high accuracy and in real time, the implemented module can perform quantile estimation and can be utilized to perform forecasting of congestion in wireless frequency spectrum utilization.

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as independent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu.

We investigate the design of signal processing in wideband spectrum usage (SPU) measurements for efficient and smart dynamic spectrum access (DSA). In particular, we focus on spectrum usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.