Future communication systems pose stringent requirements on communication latency between two nodes. One way to reduce the latency is by reducing control plane overhead by estimating the channel without training symbols. The combined process of estimating the channel and equalizing is called blind deconvolution. In this paper, our contribution is two-fold. First, we concatenate random coding (blind deconvolution using convex programming) solver with error-correcting code (ECC) decoder. Second, we propose an intelligent perturbation algorithm (IPA) that uses the ECC decoder to assist the random coding solver iteratively in solving the blind deconvolution problem. We also define the theoretical bounds for the optimal rate of the generator matrix of ECC. We have performed error-rate simulations for both Gaussian as well as Rayleigh channels. IPA provides 5 dB gain in terms of Eb/N0 over the system that uses random coding solver alone and 1 dB gain over the serial concatenation of the random coding solver and the ECC decoder, for a message length of 30 to achieve 10-3frame-error rate. IPA also provides 4 dB gain to achieve 10-3frame-error rate over the random coding solver alone, for a message length of 128.

This paper presents an algorithm to reduce the hardware complexity of the successive-cancellation decoder for polar codes. It has already been discussed in the literature that allocating 6 bits to all log-likelihood ratios instead of using floating point values, can keep performance degradation within 0.1 dB at frame-error rate of 10^-3. It has been shown in the paper that if we allocate different bits to different set of log-likelihood ratios rather than assigning the same number of bits to all log-likelihood ratios, the performance degradation is within 0.4 dB at frame-error rate of 10^-3. The results show that the proposed variable bit assignment strategy can save the processing elements in the architecture proposed by Arikan and it was observed that the bit width of N/2-1 processing elements out of total N-1 processing elements can be reduced up to 4 bits for a block length of N. In summary, this work has established a trade-off between the number of six bit processing elements and frame-error rate performance of the successive-cancellation decoder.

In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.

We analyze the general form of the extrinsic information transfer curve of polar codes viewed as multilevel codes with multistage decoding. Based on this analysis, we propose a graphical design methodology to construct polar codes for intersymbol interference channels. The method matches the extrinsic information transfer curve of the code to that of the intersymbol interference channels, so that there is an open convergence tunnel between the two. We show that polar codes can provide such a matching to the channel curve under soft-cancellation decoding and propose one such method to achieve it. Example code designs are presented to demonstrate that polar codes that are optimized for an ISI channel can significantly outperform polar codes that are designed for an AWGN channel.

The state-of-the-art soft-output decoder for polar codes is a message-passing algorithm based on belief propagation, which performs well at the cost of high processing and storage requirements. In this paper, we propose a low-complexity alternative for soft-output decoding of polar codes that offers better performance but with significantly reduced processing and storage requirements. In particular we show that the complexity of the proposed decoder is only 4% of the total complexity of the belief propagation decoder for a rate one-half polar code of dimension 4096 in the dicode channel, while achieving comparable error-rate performance. Furthermore, we show that the proposed decoder requires about 39% of the memory required by the belief propagation decoder for a block length of 32768.

A widely used soft-output decoder for polar codes is a message-passing algorithm based on belief propagation, which performs well at the cost of high processing and storage requirements. In this paper we propose a low-complexity alternative for soft-output decoding of polar codes that offers comparable performance but with significantly reduced processing and storage requirements. In particular we show that the complexity of the proposed decoder is about 5% of the total complexity of the belief propagation decoder for the dicode channel, while achieving comparable error-rate performance. Furthermore, we show that the proposed decoder requires about 39% of the memory required by the belief propagation decoder for a block length of 32768.

We describe an error-correcting system that combines polar codes with turbo equalization for partial response channels. The successive cancellation decoder, originally proposed by Arikan for polar codes, does not produce the soft outputs needed for turbo processing. The belief propagation decoder, on the other hand, requires many iterations and has high computational complexity. In this paper, we propose a soft-input soft-output variant of the successive cancellation decoder that produces the soft information required for turbo architectures, while keeping the computational complexity low. Numerical results show that the proposed decoder performs better than the hard-output successive cancellation decoder and the belief propagation decoder in the context of turbo equalization. The proposed decoder achieves this performance gain with lower complexity compared to belief propagation and maximum-likelihood decoders. Additionally, we prove that Arikan's successive cancellation decoder is a fast-polarizing instance of our soft-input soft-output successive cancellation decoder.