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We present a novel code-aided carrier recovery method for low-density parity-check (LDPC) coded systems, which comprises two supporting algorithms: 1) a coarse synchronization by maximizing a cost function, the mean absolute value of the soft outputs of the LDPC decoder, followed by a simple interpolation operation to improve the estimation accuracy and 2) a fine synchronization based on the soft decisions produced by the LDPC decoder. With moderate computational complexity, this proposed algorithm is designed to synchronize signals with large carrier frequency offset and phase offset at low signal-tonoise ratio (SNR). When applied to the case of an 8-PSK system with (1944, 972) LDPC code, the performance loss compared to the case of ideal synchronization is negligible.Index Terms-Code-aided, carrier recovery, LDPC, large carrier frequency offset, low SNR.

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.

Two complexity reducing schemes are proposed in this letter for the recently presented Kolmogorov-Smirnov (K-S) test based signal-to-noise ratio (SNR) estimator. The K-S test based SNR estimator can work properly over an extended SNR range for various multilevel constellations with limited signal samples, but involves considerably more add operations as a result for the huge amount of reference signals needed for matching operations. The proposed two complexity reducing schemes explore the order characteristic of the SNR matching pool to accelerate the searching procedure. For the situation under consideration, the computational complexities (numbers of add operation) of the two proposed schemes are about 1/5 and 1/20 of the original one respectively. Simulation results have verified these schemes' effectiveness.