A Polar Codes-Based Distributed UEP Scheme for the Internet of Things

The Internet of Things (IoT), which is expected to support a massive number of devices, is a promising communication scenario. Usually, the data of different devices has different reliability requirements. Channel codes with the unequal error protection (UEP) property are rather appealing for such applications. Due to the power-constrained characteristic of the IoT services, most of the data has short packets; therefore, channel codes are of short lengths. Consequently, how to transmit such nonuniform data from multisources efficiently and reliably becomes an issue be solved urgently. To address this issue, in this paper, a distributed coding scheme based on polar codes which can provide UEP property is proposed. The distributed polar codes are realized by the groundbreaking combination method of noisy coded bits. With the proposed coding scheme, the various data from multisources can be recovered with a single common decoder. Various reliability can be achieved; thus, UEP is provided. Finally, the simulation results show that the proposed coding scheme is viable.

Shaping duo binary turbo-coded BICM scheme for JPWL image transmission using a link adaptation strategy over wireless channels

In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.