The involvement of IEEE 802.15.4 Wireless Sensor Networks (WSNs) in diverse applications has made the realistic analysis of sensor power dissipation in distributed network environments an essential research issue. In this paper, we propose and thoroughly analyze a power dissipation model for Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) based IEEE 802.15.4 distributed multi-hop WSNs. Our model takes the loss rate of frames, neighbor sensors density in communication range of a sensor, number of hops, distance of source to the sink, and density of the network into account. We evaluate the impact of these factors on overall power dissipation. We also perform comprehensive analysis of overheads caused by message routing through multi-hop distributed networks. We validate our proposed model through Monte Carlo simulations. Results show that our power dissipation model is more realistic compared to other proposed models in terms of accuracy and multiplicity of the environments.

Due to the reuse factor reduction, the attendant increase in co-channel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

This paper studies the impact of integrating pricing with connection admission control (CAC) on the congestion management practices in contention-based wireless random access networks. Notably, when the network is free of charge, each self-interested user tries to occupy the channel as much as possible, resulting in the inefficient utilization of network resources. Pricing is therefore adopted as incentive mechanism to encourage users to choose their access probabilities considering the real-time network congestion level. A Stackelberg leader-follower game is formulated to analyze the competitive interaction between the service provider and the users. In particular, each user chooses the access probability that optimizes its payoff, while the self-interested service provider decides whether to admit or to reject the user's connection request in order to optimize its revenue. The stability of the Stackelberg leader-follower game in terms of convergence to the Nash equilibrium is established. The proposed CAC scheme is completely distributed and can be implemented by individual access points using only local information. Compared to the existing schemes, the proposed scheme achieves higher revenue gain, higher user payoff, and higher QoS performance.

Due to the reuse factor reduction, the same frequencies are reused in adjacent neighboring cells, which causes an attendant increase in co-channel interference (CCI). CCI has already become the limiting factor in the performance of orthogonal frequency division multiplexing (OFDM) based cellular systems. Joint maximum likelihood sequence estimation (JMLSE) based interference cancellation algorithms have been under intense research. However, despite the fact that the error probability of JMLSE is critical for analyzing the performance, to the best of our knowledge, the mathematical expression has not been derived for MQAM-OFDM yet. Direct computation of the error probability involves integrating a multi-dimensional Gaussian distribution that has no closed-form solution. Therefore, an alternative way is to upper and lower bound the error probability with computable quantities. In this paper, firstly, both the upper and the conventional lower error probability bounds of JMLSE are derived for MQAM-OFDM systems based on a genie-aided receiver. Secondly, in order to reduce the gap between the conventional lower bound and the simulation results, a tighter lower bound is derived by replacing the genie with a less generous one. Thirdly, those derived error probability bounds are generalized to the receiver diversity scheme. These error probability bounds are important new analytical results that can be used to provide rapid and accurate estimation of the BER performance over any MQAM scheme and an arbitrary number of interferers and receive antennas.

To decrease memory access of the decoder for double binary convolutional turbo code (DB CTC), an iterative decoding scheme is proposed. Instead of accessing all of the backward state metrics from the state metric cache (SMC), a part of them is computed by the recalculation unit (RU) in the forward direction. By analysis and simulations, both the amount of memory access and the size of SMC are reduced by about 45%. Moreover, combined with the scaling technique, the proposed scheme gets decoding performance near to that of the well-known Log-MAP algorithm.