Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.

This paper proposes a framework for evaluating mobile-to-mobile inter-system interference which occurs between two time division duplex (TDD) WiMAX systems deployed in adjacent frequency blocks and the same geographical area. The need for a new framework arises from inability of the traditional approaches to capture several important factors, such as power control and non-uniform user distribution because of high user density areas (hotspots). Accounting for these aspects becomes essential for accurately quantifying the effect of mobile-to-mobile interference for the purpose of spectrum engineering and system deployment. Based on the proposed framework, the quantitative per user performance degradation was obtained and we are also able to analyze the impact of guard band, frequency coordination between systems and partial synchronization on mobile-to-mobile interference.

LDPC codes are analyzed and optimized for a noncoherent block fading channel in which there is correlation between the blocks. This extends related work in the literature which considered independent blocks. Low-complexity estimation and estimation-decoding algorithms based on Kalman smoothing are developed, and density evolution is used to analyze and optimize the resulting code. Results indicate that significant gains can be realized by taking inter-block correlation into account, compared to architectures in which the blocks are assumed to be independent.

As the standardization of Evolved-UTRA or LTE is ongoing in 3GPP, it is important to ensure the coexistence between two LTE systems or between LTE and legacy UMTS systems sharing the same frequency band. This paper is aimed at presenting a complete view of the status and challenges of the coexistence issue. The high-level methodology, UL power control, and ACLR modeling, etc. are discussed in detail. To ensure coexistence without tightening the RF requirements and hence increasing the cost for LTE, mitigation techniques like interference-aware resource allocation are proposed and shown to be effective by simulation results.

A receiver structure that uses decision feedback and successive decoding is proposed for a noncoherent channel with independent block fading. The transceiver employs a block interleaver to decompose the channel into a set of independent fading sub-channels. LDPC codes are used on each sub-channel, and the decoded data are fed back to help in channel estimation. Simulation results indicate that the performance of the proposed system is within 0.7 dB of the Shannon capacity of the binary-input block fading channel.

This paper proposes novel successive decoding paradigm for fading channels, K independent codewords of different rates and lengths are interleaved at the transmitter according to a pattern, and successively decoded at the receiver. This enables progressively better channel estimates for the receiver, at a minimal cost of pilot symbols. At each decoding stage, channel estimation and decoding are performed separately. or iteratively if the residual channel correlation within a codeword can be exploited. The flexible interleaver pattern, after some level of optimization, achieves near-capacity system performance with only a small number of codewords K, as demonstrated numerically by a finite state Markov modelled flat fading channel

This paper proves that low density parity check (LDPC) codes are universal codes on correlated fading channels if a successive decoding receiver is used. A universal LDPC code is defined as a code with the same performance over a class of channels, in which the performance is measured by the threshold of the code in terms of mutual information (in bits/sec/Hz). The receiver proposed in this paper decomposes the fading channel into a bank of memoryless sub-channels. Each sub-channel is encoded with a LDPC code. An MMSE estimator followed by a LDPC decoder is used to successively decodes the channel code. With this scheme, we show that LDPC codes have the universal performance on fading channels with variant fading rates. We also prove that the LDPC code design is unified. Hence, the optimal LDPC codes for the perfect CSI receiver is also optimal for correlated fading channels without CSI.

This paper derives an iterative receiver for noncoherent fading channels that exhibit block fading with correlation between blocks. Pilot symbols and Kalman smoothing are used in conjunction with sum-product decoding of LDPC codes to implement an iterative channel estimation and decoding structure. Density evolution is employed to analyze the performance of such a structure and to optimize the degree profile of LDPC codes. The resulting receiver performs significantly better than receivers in which channel estimation and decoding are carried out in a tandem (non-iterative) fashion

This paper presents a new coding scheme with capacity approaching performance for correlated fading channels with unknown state. The transceiver employs a deep interleaver to decompose the original channel into a bank of independent fading sub-channels. Sub-channels are successively decoded and decisions are fed back as new training symbols. The unknown state of each sub-channel can be estimated from past channel observations and future channel outputs. We show that the sub-channel has the same capacity as the original channel, given a sufficiently large estimation window. Therefore, the correlated fading channel capacity can be approached if each sub-channel uses an optimized capacity achieving code. Simulations show a universal performance within 1 dB of capacity upper bound regardless of fading rate. The scheme is also robust against decision feedback errors when the sub-channels use a long LDPC code

This paper presents a density evolution analysis of the sum-product algorithm used for channel estimation and decoding of low density parity check (LDPC) codes on correlated and uncorrelated two-state block fading channels. The channels under consideration use binary symmetric channels and binary-input Gaussian channels as components, and the thresholds for regular LDPC codes on these channels are calculated. The analysis shows that for both correlated and uncorrelated block fading channels, the threshold increases with the memory length. If the memory length is fixed, introducing correlation between successive blocks increases the threshold; as the memory length increases, this effect diminishes

To realize their full coding gain potential, iterative decoders need to operate in low signal-to-noise ratio (SNR) regions where timing recovery devices experience cycle-slips. This paper develops a binary Bayesian cycle-slip detector by using soft-output information. A practical decision rule is derived utilizing a series of approximations. Approximate analytic expressions for the probabilities of cycle-slip detection and false alarm are obtained. They guide us in choosing detector parameters that will meet a desired performance specification. The applicability of this method is shown on artificially generated waveforms.

Abstract This paper describes an implementation and performance evaluation of different deadlock prevention algorithms. A deadlock prevention algorithm ensures that deadlock will never happen. The algorithms for deadlock prevention are proposed and implemented in a locally distributed system. A number of experiments were executed in a distributed system for various lengths of file operation and different numbers of files. The performance of the system and of each algorithm is evaluated and discussed. Some general results are derived for a single‐host and a distributed system.