Abstract
In recent years, many efforts have been made to find the optimal labeling maps for bit-interleaved coded modulation with iterative decoding (BICM-ID) with the aim to exploit the benefits of iterative decoding to the maximal extent. The current paper reveals new opportunities that BICM-ID signal labeling brings for system designers: it enables recipient addressing without any explicitly sent identifier. Instead, the recipient ID is represented by a labeling map, selected from a set of equally optimal labelings. A simple method of frame filtering, necessary to retrieve the desired data frame at the receiver side, is proposed and evaluated. It is also shown that the proposed Labeling-Based Recipient Identification (LABRID) approach does not cause any inferior performance outcome in terms of bit error rate.
Highlights
In a communication system, a message, to reach the final recipient, usually passes through several nodes and links, possibly over physical media of different types
The data frame (i.e., protocol data unit (PDU) of Medium Access Control (MAC) sublayer) is encapsulated in a so-called physical layer PDU (PPDU), whose structure depends on the coding scheme, modulation order, etc., for the given physical medium
From the literature (e.g., [13]), it is known that short-memory codes result in early decoding convergence, whereas long-memory codes give steeper error-free feedback (EF) bounds at the cost of shifting the turbo cliff towards the right on the bit error rate (BER) vs. Eb/N0 plot
Summary
A message, to reach the final recipient, usually passes through several nodes and links, possibly over physical media of different types (twisted pair, wireless channel, etc.). The data frame (i.e., PDU of MAC sublayer) is encapsulated in a so-called physical layer PDU (PPDU), whose structure depends on the coding scheme, modulation order, etc., for the given physical medium. The paper just contributes the idea, and does not focus on changes in the TCP/IP protocol stack, necessary to enable dropping the recipient identifier to the physical layer instead of burying it into the data frame, etc. The iterative decoding process can converge if the following conditions are met: sufficient size of data frame, enough signal/noise ratio, and appropriate signal labeling. There is a so-called turbo cliff region on the BER vs signal/noise ratio (SNR) plot, where experimental curves decline steeply and meet the so-called error-free feedback (EF) bound. From the literature (e.g., [13]), it is known that short-memory codes result in early decoding convergence (i.e., the turbo cliff appears at low Eb/N0 region), whereas long-memory codes give steeper EF bounds at the cost of shifting the turbo cliff towards the right on the BER vs. Eb/N0 plot
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