A comparative study on the modified Max-Log-MAP turbodecoding by extrinsic information scaling

A simple but effective technique to improve the performance of the Max-Log-MAP algorithm is to scale the extrinsic information exchanged between two MAP decoders. A comprehensive analysis of the selection of the scaling factors according to channel conditions and decoding iterations is presented in this paper. Choosing a constant scaling factor for all SNRs and iterations is compared with the best scaling factor selection for changing channel conditions and decoding iterations. It is observed that a constant scaling factor for all channel conditions and decoding iterations is the best solution and provides a 0.2-0.4 dB gain over the standard Max- Log-MAP algorithm. Therefore, a constant scaling factor should be chosen for the best compromise.

The modified Max-Log-MAP turbo decoding algorithm by extrinsic information scaling for wireless applications

The iterative nature of turbo-decoding algorithms increases their complexity compare to conventional FEC decoding algorithms. Two iterative decoding algorithms, Soft-Output-Viterbi Algorithm (SOVA) and Maximum A posteriori Probability (MAP) Algorithm require complex decoding operations over several iteration cycles. So, for real-time implementation of turbo codes, reducing the decoder complexity while preserving bit-error-rate (BER) performance is an important design consideration. In this chapter, a modification to the Max-Log-MAP algorithm is presented. This modification is to scale the extrinsic information exchange between the constituent decoders. The remainder of this chapter is organized as follows: An overview of the turbo encoding and decoding processes, the MAP algorithm and its simplified versions the Log-MAP and Max-Log-MAP algorithms are presented in section 1. The extrinsic information scaling is introduced, simulation results are presented, and the performance of different methods to choose the best scaling factor is discussed in Section 2. Section 3 discusses trends and applications of turbo coding from the perspective of wireless applications.

Old conflicts, new rivals? Airline competition in the European market

Christoph Franz of Lufthansa recently identified Ryanair, easyJet, Air Berlin and Emirates as the company’s main competitors – gone are the days when it could benchmark itself against BA or Air France-KLM! This paper probes behind the headlines to assess the extent to which different airlines are in competition, using evidence from the UK and mainland European markets. The issue of route versus network competition is addressed. Many regulators have put an emphasis on the former whereas the latter, although less obvious, can be more relevant. For example, BA and American will cease to compete between London and Dallas Fort Worth if their alliance obtains anti-trust immunity but 80% of the passengers on this route are connecting at one or both ends and hence arguably belong to different markets (e.g. London-San Francisco, Zurich-Dallas, Edinburgh-New Orleans) which may be highly contested. The remaining 20% of local traffic is actually insufficient to support a single point to point service in its own right. Estimates are made of the seat capacity major airlines are offering to the local market as distinct from feeding other routes. On a sector such as Manchester–Amsterdam, 60% of KLM’s passengers are transferring at Schiphol as against only 1% of bmibaby’s. Thus although KLM operates 5 flights and 630 seats per day against bmibaby’s 2 flights and 298 seats, in the point to point market bmibaby offers more seats than KLM. The growth of the Low Cost Carriers (LCCs) means that competition increasingly needs to be viewed on city pair markets (e.g. London-Rome) rather than airport pair markets (e.g. Heathrow-Fiumicino). As the stronger LCCs drive out weaker rivals and mainline carriers retrench to their major hubs, some markets now have fewer direct options than existed prior to the low cost boom. Timings and frequencies are considered, in particular the extent to which services are a true alternative especially for business travellers. LCCs typically offer lower frequencies and more unsociable timings (e.g. late evening arrivals at remote airports) as they are more focused on providing the cheapest service rather than the most convenient schedule. Interesting findings on ‘monopoly’ services are presented (including alliances) - certain airlines have many more of these than others. Lufthansa has a significant number of sectors to itself whereas at the other extreme British Airways has direct competition on almost every route in its network. Ryanair and flybe have a higher proportion of monopoly routes than easyJet or Air Berlin. In the domestic US market it has become apparent since deregulation that better financial returns can come from dominating a large number of smaller markets rather than being heavily exposed in the major markets - which are hotly fought over. Regional niches that appear too thin for Ryanair to serve (with its all 189 seat 737-800 fleet) are identified. Fare comparisons in contrasting markets provide some insights to marketing and pricing strategies. Data sources used include OAG (schedules and capacity), AEA (traditional European airlines traffic by region), the UK CAA (airport, airline and route traffic plus survey information of passenger types) and ICAO (international route traffic and capacity by carrier). It is concluded that airlines often have different competitors depending on the context but in surprisingly many cases there are actually few or no direct substitutes. The competitive process set in train by deregulation of European air services in the 1990s is leading back to one of natural monopolies and oblique alternatives. It is the names of the main participants that have changed however!