Fronthaul evolution: From CPRI to Ethernet

It is proposed that using Ethernet in the fronthaul, between base station baseband unit (BBU) pools and remote radio heads (RRHs), can bring a number of advantages, from use of lower-cost equipment, shared use of infrastructure with fixed access networks, to obtaining statistical multiplexing and optimised performance through probe-based monitoring and software-defined networking. However, a number of challenges exist: ultra-high-bit-rate requirements from the transport of increased bandwidth radio streams for multiple antennas in future mobile networks, and low latency and jitter to meet delay requirements and the demands of joint processing. A new fronthaul functional division is proposed which can alleviate the most demanding bit-rate requirements by transport of baseband signals instead of sampled radio waveforms, and enable statistical multiplexing gains. Delay and synchronisation issues remain to be solved

Cloud RAN for Mobile Networks - a Technology Overview

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user’s needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on base- band resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This article surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RA

Complex Block Floating-Point Format with Box Encoding For Wordlength Reduction in Communication Systems

We propose a new complex block floating-point format to reduce implementation complexity. The new format achieves wordlength reduction by sharing an exponent across the block of samples, and uses box encoding for the shared exponent to reduce quantization error. Arithmetic operations are performed on blocks of samples at time, which can also reduce implementation complexity. For a case study of a baseband quadrature amplitude modulation (QAM) transmitter and receiver, we quantify the tradeoffs in signal quality vs. implementation complexity using the new approach to represent IQ samples. Signal quality is measured using error vector magnitude (EVM) in the receiver, and implementation complexity is measured in terms of arithmetic complexity as well as memory allocation and memory input/output rates. The primary contributions of this paper are (1) a complex block floating-point format with box encoding of the shared exponent to reduce quantization error, (2) arithmetic operations using the new complex block floating-point format, and (3) a QAM transceiver case study to quantify signal quality vs. implementation complexity tradeoffs using the new format and arithmetic operations.Comment: 6 pages, 9 figures, submitted to Asilomar Conference on Signals, Systems, and Computers 201

Landscape of IoT security

The last two decades have experienced a steady rise in the production and deployment of sensing-and-connectivity-enabled electronic devices, replacing “regular” physical objects. The resulting Internet-of-Things (IoT) will soon become indispensable for many application domains. Smart objects are continuously being integrated within factories, cities, buildings, health institutions, and private homes. Approximately 30 years after the birth of IoT, society is confronted with significant challenges regarding IoT security. Due to the interconnectivity and ubiquitous use of IoT devices, cyberattacks have widespread impacts on multiple stakeholders. Past events show that the IoT domain holds various vulnerabilities, exploited to generate physical, economic, and health damage. Despite many of these threats, manufacturers struggle to secure IoT devices properly. Thus, this work overviews the IoT security landscape with the intention to emphasize the demand for secured IoT-related products and applications. Therefore, (a) a list of key challenges of securing IoT devices is determined by examining their particular characteristics, (b) major security objectives for secured IoT systems are defined, (c) a threat taxonomy is introduced, which outlines potential security gaps prevalent in current IoT systems, and (d) key countermeasures against the aforementioned threats are summarized for selected IoT security-related technologies available on the market