Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators

The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].

Enabling Intelligence in Spectrum Sharing

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that the goal for a disruptive innovation, in the future mobile network architecture, that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a \emph{real-time bidding} manner. We also introduce a spectrum embedding technique that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several significant challenges that need to be addressed in practical deployment are investigated.

Enabling Intelligence in Spectrum Sharing

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that the goal for a disruptive innovation, in the future mobile network architecture, that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a \emph{real-time bidding} manner. We also introduce a spectrum embedding technique that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several significant challenges that need to be addressed in practical deployment are investigated.

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

Federated Learning for 5G Radio Spectrum Sensing

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

On Outage Analysis in Overlay CCRN With RF Energy Harvesting and Co-Channel Interference

This paper explores the impact of co-channel interference (CCI) on the link outage and radio frequency (RF) energy harvesting (EH). For analysis, co-operative cognitive radio network (CCRN) architecture is considered as system model that supports one-way primary user (PU) and two-way secondary user (SU) communications, using an overlay mode of spectrum sharing. Closed form outage expressions are derived for both PU and SU network in presence of multiple antennas at PUs and CCI at SUs. The effect of CCI on the system performance is studied with respect to interference-to-noise-ratio (INR), transmission power, number of antennas and number of CCI sources. Performance gains are found to achieve ~ 20% and ~ 15% for PU and SU outage in two antenna system over a single antenna one.

Resource Allocation Algorithm Based on Power Control and Dynamic Transmission Protocol Configuration for HAPS-IMT Integrated System

The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, “low power almost-bank subframe (LP-ABS)”, is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in “LP-ABS”. Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.

High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.