Concurrent Backscatter Streaming from Batteryless and Wireless Sensor Tags with Multiple Subcarrier Multiple Access

This paper proposes a novel multiple access method that enables concurrent sensor data streaming from multiple batteryless, wireless sensor tags. The access method is a pseudo-FDMA scheme based on the subcarrier backscatter communication principle, which is widely employed in passive RFID and radar systems. Concurrency is realized by assigning a dedicated subcarrier to each sensor tag and letting all sensor tags backscatter simultaneously. Because of the nature of the subcarrier, which is produced by constant rate switching of antenna impedance without any channel filter in the sensor tag, the tag-to-reader link always exhibits harmonics. Thus, it is important to reject harmonics when concurrent data streaming is required. This paper proposes a harmonics rejecting receiver to allow simultaneous multiple subcarrier usage. This paper particularly focuses on analog sensor data streaming which minimizes the functional requirements on the sensor tag and frequency bandwidth. The harmonics rejection receiver is realized by carefully handling group delay and phase delay of the subcarrier envelope and the carrier signal to accurately produce replica of the harmonics by introducing Hilbert and inverse Hilbert transformations. A numerical simulator with Simulink and a hardware implementation with USRP and LabVIEW have been developed. Simulations and experiments reveal that even if the CIR before harmonics rejection is 0dB, the proposed receiver recovers the original sensor data with over 0.98 cross-correlation

Future RAN architecture: SD-RAN through a general-purpose processing platform

In this article, we identify and study the potential of an integrated deployment solution for energy-efficient cellular networks combining the strengths of two very active current research themes: 1) software-defined radio access networks (SD-RANs) and 2) decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency. While SD-RAN envisions a decoupled centralized control plane and data-forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from the capacity and coverage provided through an always-on low-power signaling node for a larger geographical area; the capacity is catered by various on-demand data nodes for maximum energy efficiency. In this article, we show that a combined approach that brings both specifications together can not only achieve greater benefits but also facilitate faster realization of both technologies. We propose the idea and design of a signaling controller that acts as a signaling node to provide always-on coverage, consuming low power, and at the same time host the control plane functions for the SDRAN through a general-purpose processing platform. The phantom cell concept is also a similar idea where a normal macrocell provides interference control to densely deployed small cells, although our initial results show that the integrated architecture has a much greater potential for energy savings than phantom cells

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner