Turbo Decoding and Detection for Wireless Applications

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119

126

Abstract-Near-capacity performance may be achieved with the aid of iterative decoding, where extrinsic soft information is exchanged between the constituent decoders in order to improve the attainable system performance. Extrinsic Information Transfer (EXIT) charts constitute a powerful semi-analytical tool used for analysing and designing iteratively decoded systems. In this tutorial, we commence by providing a rudimentary overview of the iterative decoding principle and the concept of soft information exchange. We then elaborate on the concept of EXIT charts using three iteratively decoded prototype systems as design examples. We conclude by illustrating further applications of EXIT charts, including near-capacity designs, the concept of irregular codes and the design of modulation schemes.

Section: B Exit Chart Analysis Of a Two-stage Serially Concatenated mentioning

confidence: 99%

EXIT Charts for System Design and Analysis

El‐Hajjar

Hanzo

2014

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119

126

“…We will show that the calculations of each step can be decomposed into simple Add-Compare-Select (ACS) operations. Further detailed discussions are available in [18], [35].…”

Section: E Log-bcjr Decodermentioning

confidence: 99%

20 Years of Turbo Coding and Energy-Aware Design Guidelines for Energy-Constrained Wireless Applications

Brejza

Maunder

et al. 2016

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Abstract-During the last two decades, wireless communication has been revolutionized by near-capacity Error-Correcting Codes (ECCs), such as Turbo Codes (TCs), which offer a lower Bit Error Ratio (BER) than their predecessors, without requiring an increased transmission Energy Consumption (EC). Hence, TCs have found widespread employment in spectrum-constrained wireless communication applications, such as cellular telephony, Wireless Local Area Network (WLAN) and broadcast systems. Recently however, TCs have also been considered for energyconstrained wireless communication applications, such as Wireless Sensor Networks (WSNs) and the 'Internet of Things' (IoT). In these applications, TCs may also be employed for reducing the required transmission EC, instead of improving the BER. However, TCs have relatively high computational complexities and hence the associated signal-processing-related ECs are not insignificant. Therefore, when parameterizing TCs for employment in energy-constrained applications, both the processing EC and the transmission EC must be jointly considered. In this tutorial, we investigate holistic design methodologies conceived for this purpose. We commence by introducing turbo coding in detail, highlighting the various parameters of TCs and characterizing their impact on the encoded bit rate, on the Radio Frequency (RF) bandwidth requirement, on the transmission EC and on the BER. Following this, energy-efficient TC decoder ApplicationSpecific Integrated Circuit (ASIC) architecture designs are exemplified and the processing EC is characterized as a function of the TC parameters. Finally, the TC parameters are selected in order to minimize the sum of the processing EC and the transmission EC.

“…At the receiver, iterative exchange of soft information is carried out between the two so-called Bahl, Cocke, Jelinek and Raviv (BCJR) decoders [5]. The BCJR decoder is also often referred to as the Maximum A posteriori (MAP) algorithm, which estimates a decoded bit by selecting the specific transition path having the maximum a posteriori probability among all transition paths from one state to the next in the decoder's trellis [6]. Since the calculation of each transition probability involves the exploration of all possible paths through the trellis, the complexity of the BCJR decoder is potentially high, especially when several iterations are used for exchanging soft information between two BCJR decoders.…”

Section: A Turbo Code Complexitymentioning

confidence: 99%

A Survey and Tutorial on Low-Complexity Turbo Coding Techniques and a Holistic Hybrid ARQ Design Example

Chen

Maunder

Hanzo

2013

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Abstract-Hybrid Automatic Repeat reQuest (HARQ) has become an essential error control technique in communication networks, which relies on a combination of arbitrary error correction codes and retransmissions. When combining turbo codes with HARQ, the associated complexity becomes a critical issue, since conventionally iterative decoding is immediately activated after each transmission, even though the iterative decoder might fail in delivering an error-free codeword even after a high number of iterations. In this scenario, precious battery-power would be wasted. In order to reduce the associated complexity, we will present design examples based on Multiple Components Turbo Codes (MCTCs) and demonstrate that they are capable of achieving an excellent performance based on the lowest possible memory octally represented generator polynomial (2, 3)o. In addition to using low-complexity generator polynomials, we detail two further techniques conceived for reducing the complexity. Firstly, an Early Stopping (ES) strategy is invoked for curtailing iterative decoding, when its Mutual Information (MI) improvements become less than a given threshold. Secondly, a novel Deferred Iteration (DI) strategy is advocated for the sake of delaying iterative decoding, until the receiver confidently estimates that it has received sufficient information for successful decoding. Our simulation results demonstrate that the MCTC aided HARQ schemes are capable of significantly reducing the complexity of the appropriately selected benchmarkers, which is achieved without degrading the Packet Loss Ratio (PLR) and throughput. codes. Figure 1.1 of [1] outlines the brief history of FEC codes. The ratio of the number of information bits to the total number of information and parity bits defines the normalized throughput or the coding rate. Shannon's channel capacity determines the upper bound of the coding rate that any FEC code may be able to achieve at a certain Signal Noise Ratio (SNR). Since the transmission of these parity bits requires an increased bandwidth, the maximum coding rate that an FEC code can have, while still recovering the information bits becomes a useful criterion for quantifying the capability of FEC codes. Their decoding complexity is also a critical factor in the evaluation of FEC codes. Researchers have studied the associated tradeoff between these two aspects, when choosing a specific FEC code for a communication system. I. TURBO CODING COMPLEXITY A. Turbo Code ComplexityAs one of the most powerful codes in the FEC family, turbo codes [4] have shown capacity-achieving capability by combining two parallel concatenated Recursive Systematic Convolutional (RSC) codes at the transmitter. At the receiver, iterative exchange of soft information is carried out between the two so-called Bahl, Cocke, Jelinek and Raviv (BCJR) decoders [5]. The BCJR decoder is also often referred to as the Maximum A posteriori (MAP) algorithm, which estimates a decoded bit by selecting the specific transition path having the maximum a posteriori pr...