This paper addresses the blind recovery of the parity check matrix of an (n, k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on a k-by-k full-rank matrix and uses a threshold and the reliability associated to verify the recovered dual words, aiming to improve the reliability of recovery. Finally, it performs decoding on the received codewords with partially recovered dual words. These three parts can be combined into different schemes for different noise level scenarios. The GEV that combines Gaussian elimination and verification has a significantly lower recovery failure probability and a much lower computational complexity than an existing Canteaut-Chabaud-based algorithm, which relies on GE on n-by-n full-rank matrices. The decoding-aided recovery (DAR) and error-detection-&-codeword-selection-&-decoding-aided recovery (EDCSDAR) schemes can improve the code recovery performance over GEV for high noise level scenarios, and their computational complexities remain much lower than the Canteaut-Chabaud-based algorithm.

The ambient backscatter communication (AmBC) system enables radio-frequency (RF) powered devices (e.g., tags, sensors) to transmit their information bits to readers by backscattering and modulating the ambient RF signal. Different from traditional radio-frequency identification (RFID) systems, an AmBC system does not require a reader to transmit excitation signals to the tag and there is no additional carrier emitters required. Therefore, AmBC systems exhibit low-cost and high energy efficiency. The existing AmBC systems utilize an energy detector or a Minimum Mean Square Error (MMSE) detector to detect tag signals which suffers from high bit error rate (BER). In this paper, a machine learning based detection method is proposed to detect the tag signals for an AmBC system by transforming the detection problem into a classification problem. In more detail, the proposed method classifies the received signals into two groups based on the energy features of the received signals. Our simulation results show that the proposed machine learning based detection method outperforms the traditional detection methods, especially in the low SNR regime.

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. From the investigated set of parameters, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs. It is of significant interest to further study the generality of our conclusion in other network models and with other parameter sets.

Wireless broadcast has been increasingly used to deliver information of common interest to a large number of users. There are two major challenges in wireless broadcast: the unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large. In this paper, by resorting to stochastic geometry analysis, we develop a network coding based broadcast scheme that allows a base station (BS) to broadcast a given number of packets to a large number of users, without user acknowledgment, while being able to provide a performance guarantee on the probability of successful delivery. Further, the BS only has limited statistical information about the environment including the spatial distribution of users (instead of their exact locations and number) and the wireless propagation model. Performance analysis is conducted. On that basis, an upper and a lower bound on the number of packet transmissions required to meet the performance guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper are useful for designing efficient and reliable wireless broadcast strategies.

There is an increasing demand for broadcasting information of common interest to a large number of users. The unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large are two major challenges for wireless network broadcasting. In this paper we investigate the problem that a base station broadcasts a given number of packets to a given number of users, without user acknowledgment, while being able to provide a guarantee on the probability of successful delivery. Network coding technique is employed to improve both the efficiency and the reliability of the broadcast. Performance analysis is conducted. Based on the analysis, an upper and a lower bound on the number of packet transmissions required to meet the reliability guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper can be useful for designing strategies to deliver information of common interest to a large number of users efficiently and reliably.

Wireless broadcast has been widely utilized to deliver information of common interest to a large number of users. A major challenge for wireless broadcast is that wireless links are often unreliable. Further, it is not feasible for every receiver to acknowledge the correct reception of broadcasted packets. In this paper we investigate the use of wireless broadcast to deliver a given number of packets by a common transmitter to a given number of receivers, without feedback from the receivers, while meeting the reliability constraint, i.e. the probability that all receivers successfully receive all broadcasted packets is above a certain threshold. Rateless codes(RCs) technology is used to assist the broadcast. Performance analysis with the use of RCs is conducted. Simulations are conducted to validate the accuracy of the theoretical analysis. It is shown that the use of RCs can significantly reduce the number of transmissions required to meet the reliability constraint.