This tutorial paper addresses the physical layer security concerns and resiliency of Orthogonal Frequency Division Multiplexing (OFDM) communications; the de facto air-interface of most modern wireless broadband standards including 3GPP Long Term Evolution (LTE) and WiMAX. The paper starts with a brief introduction to the OFDM waveform and then reviews the robustness of the existing OFDM waveform in the presence of noise, multipath fading, and interference. The paper then moves on to build comprehensive adversarial models against OFDM waveforms. Robustness of OFDM is first investigated under AWGN noise and noise-like jamming attack scenarios, then under uncorrelated yet colored interferences from modulated sources (both intentional and unintentional). Finally, the paper explores some of the more recent developments in the field of energy efficient correlated jamming attacks that can disrupt communication severely by exploiting the knowledge of the target waveform structure. Potential countermeasures against such jamming attacks are presented, in an attempt to make a robust and resilient OFDM waveform.

PHY-Layer Resiliency in OFDM Communications: A Tutorial

We consider a relay communication with distributed channel coding. The source broadcasts channel encoded and modulated information to the relay and to the destination. In a second time slot, the relay sends additional redundancy to the destination. The broadcast from the source leads to the dilemma of adjusting the modulation scheme to the relay or to the destination link. We propose to use hierarchical modulation to solve this dilemma. We show with simulation results that the proposed system achieves a significant gain compared to reference systems.

Relay communication with hierarchical modulation

Mobile networks of the future are predicted to be much denser than today's networks in order to cater to increasing user demands. In this context, cloud based radio access networks have garnered significant interest as a cost effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best knowledge of the authors, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10 to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.

Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective?

Despite commercial 3G networks are starting to be fully operational and high speed data packet access (HSDPA) is on its way to be deployed, operators and manufacturers are already in a race towards 4G technologies. The road to 4G has a mandatory milestone in long term evolution (LTE) as it is a promising technology which will allow backwards compatibility besides a higher performance. Thus operators, manufacturers and research institutes are cooperating in the definition of LTE specifications. This paper presents a performance evaluation of LTE downlink physical layer according to the latest 3GPP specifications. Particularly, the main features at the LTE physical layer (like spatial multiplexing or adaptive modulation and coding) are described and analyzed.

Physical Layer Performance of Long Term Evolution Cellular Technology

For decades, the de facto standard for forward error correction was a convolutional code decoded with the Viterbi algorithm, often concatenated with another code (e.g., a Reed-Solomon code). But since the introduction of turbo codes in 1993, much more powerful codes referred to collectively as turbo and turbo-like codes have eclipsed classical methods. These powerful error-correcting techniques achieve excellent error-rate performance that can closely approach Shannon's channel capacity limit. The lure of these large coding gains has resulted in their incorporation into a widening array of telecommunications standards and systems. This paper will briefly characterize turbo and turbo-like codes, examine their implications for physical layer system design, and discuss standards and systems where they are being used. The emphasis will be on telecommunications applications, particularly wireless, though others are mentioned. Some thoughts on the use of turbo and turbo-like codes in the future will also be given.

Turbo and Turbo-Like Codes: Principles and Applications in Telecommunications

This paper provides a description of the turbo code used by the UMTS third-generation cellular standard, as standardized by the Third-Generation Partnership Project (3GPP), and proposes an efficient decoder suitable for insertion into software-defined radio architectures or for use in computer simulations. Because the decoder is implemented in software, rather than hardware, single-precision floating-point arithmetic is assumed and a variable number of decoder iterations is not only possible but desirable. Three twists on the well-known log-MAP decoding algorithm are proposed: (1) a linear approximation of the correction function used by the max* operator, which reduces complexity with only a negligible loss in BER performance; (2) a method for normalizing the backward recursion that yields a 12.5% savings in memory usage; and (3) a simple method for halting the decoder iterations based only on the log-likelihood ratios.

The UMTS Turbo Code and an Efficient Decoder Implementation Suitable for Software-Defined Radios

Turbo codes are sensitive to both (timing) synchro- nization errors and signal-to-noise ratio (SNR) mismatch. Since turbo codes are intended to work in environments with very low SNR, conventional synchronization methods often fail. This paper investigates blind symbol-timing synchronization and SNR estimation based on oversampled data frames. The technique is particularly suitable for low-rate turbo codes operating in addi- tive white Gaussian noise at low SNR and modest data-transfer rates, as in deep space, satellite, fixed wireless, or wireline com- munications. In accordance with the turbo principle, intermediate decoding results are fed back to the estimator, thereby facili- tating decision-directed estimation. The analytical and simulated results show that with three or more samples per symbol and raised cosine-rolloff pulse shaping, performance approaches that of systems with perfect timing and SNR knowledge at the receiver.

Transactions Papers Joint Synchronization and SNR Estimation for Turbo Codes in AWGN Channels

Turbo codes are sensitive to both (timing) synchronization errors and signal-to-noise ratio (SNR) mismatch. Since turbo codes are intended to work in environments with very low SNR, conventional synchronization methods often fail. This paper investigates blind symbol-timing synchronization and SNR estimation based on oversampled data frames. The technique is particularly suitable for low-rate turbo codes operating in additive white Gaussian noise at low SNR and modest data-transfer rates, as in deep space, satellite, fixed wireless, or wireline communications. In accordance with the turbo principle, intermediate decoding results are fed back to the estimator, thereby facilitating decision-directed estimation. The analytical and simulated results show that with three or more samples per symbol and raised cosine-rolloff pulse shaping, performance approaches that of systems with perfect timing and SNR knowledge at the receiver.

Joint synchronization and SNR estimation for turbo codes in AWGN channels

Publisher Summary
This chapter deals with turbo codes, one of the most powerful types of forward-error-correcting channel codes. It discusses the underlying concepts and presents a description and comparison of the turbo codes used by the Universal Mobile Telecommunications System (UMTS) and cdma2000 third-generation cellular systems. Forward-error-correcting (FEC) channel codes are commonly used to improve the energy efficiency of wireless communication systems. By the end of the 1990s, the virtues of turbo codes were well known, and they began to be adopted in various systems. One of the most interesting characteristics of a turbo code is that it is not just a single code. This chapter illustrates the performance of the turbo codes used by the two third-generation cellular standards. Although turbo codes have the potential to offer unprecedented energy efficiencies, they have some peculiarities, which result in error flooring. A way to reduce the error floor is to arrange the two constituent encoders in a serial concatenation, rather than in a parallel concatenation. One solution to the synchronization problems is to incorporate the synchronization process into the iterative feedback loop of the turbo decoder itself.

Chapter 12 – Turbo Codes

Turbo codes are sensitive to both (timing) synchronization errors and signal-to-noise ratio (SNR) mismatch. Since turbo codes are intended to be deployed in environments with very low SNR, conventional synchronization methods often fail. This paper introduces a solution for jointly estimating the SNR and achieving timing synchronization based on the statistics of the received signal. Simulation results show only a small loss in coding gain relative to perfect timing and SNR estimation while requiring only slightly more complexity and latency.

Jian Sun

Papers

The UMTS Turbo Code and an Efficient Decoder Implementation Suitable for Software-Defined Radios

Transactions Papers Joint Synchronization and SNR Estimation for Turbo Codes in AWGN Channels

Joint synchronization and SNR estimation for turbo codes in AWGN channels

Chapter 12 – Turbo Codes

Synchronization of turbo codes based on online statistics