An Opportunistic Error Correction Layer for OFDM Systems

In this paper, we propose a novel cross layer scheme to lower power\ud consumption of ADCs in OFDM systems, which is based on resolution\ud adaptive ADCs and Fountain codes. The key part in the new proposed\ud system is that the dynamic range of ADCs can be reduced by\ud discarding the packets which are transmitted over 'bad' sub\ud carriers. Correspondingly, the power consumption in ADCs can be\ud reduced. Also, the new system does not process all the packets but\ud only processes surviving packets. This new error correction layer\ud does not require perfect channel knowledge, so it can be used in a\ud realistic system where the channel is estimated. With this new\ud approach, more than 70% of the energy consumption in the ADC can be\ud saved compared with the conventional IEEE 802.11a WLAN system under\ud the same channel conditions and throughput. The ADC in a receiver\ud can consume up to 50% of the total baseband energy. Moreover, to\ud reduce the overhead of Fountain codes, we apply message passing and\ud Gaussian elimination in the decoder. In this way, the overhead is\ud 3% for a small block size (i.e. 500 packets). Using both methods\ud results in an efficient system with low delay

Turbo Decoding and Detection for Wireless Applications

A historical perspective of turbo coding and turbo transceivers inspired by the generic turbo principles is provided, as it evolved from Shannon’s visionary predictions. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannon’s capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated spacetime and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted

Signal estimation in cognitive satellite networks for satellite-based industrial internet of things

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M 2 M ∞ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT