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In recent years, user cooperative traffic forwarding is a popular study topic and broadly seen as one of the important promising technologies to improve energy efficiency (EE) of the battery-driven mobile terminal (MT). However, the battery-driven devices always suffer from a problem of limited working time due to battery life. In this paper, we propose a simply machine learnable bandwidth allocation strategy for user cooperation-aided wireless communication systems and evaluate the power consumption of the systems via both theoretical and experimental approaches. By using the proposed bandwidth allocation strategy, we first derive the mathematical expressions to evaluate the transmission power of the MTs for non-cooperative and cooperative scenarios by a generalized channel model. In this generalized model, the spatially correlated shadowing and frequency selective fading are considered as channel effects, and this generalized model is mathematically analyzed for the consumed power via the proposed scenarios with the long-term evolution (LTE) power model for smartphones. In the final stage, we evaluate the results by our smartphone test-bed. The results obtained in this paper show that the benefits of the user cooperation-aided traffic forwarding are significant. Unfortunately, according to the numerical analysis, because there are some physical constraints for MTs, such as maximal transmit power, we cannot drastically obtain the benefits in real application cases. Some interesting points, such as how to use a machine learning approach to reduce the system complexity and thus improve transmission performances, are also discussed in this paper.

Recently, we proposed an interference-aware channel segregation based dynamic channel assignment (IACS-DCA). In IACS-DCA, each base station (BS) measures the instantaneous co-channel interference (CCI) power on each available channel, computes the moving average CCI power using past CCI measurement results, and selects the channel having the lowest moving average CCI power. In this way, the CCI-minimized channel reuse pattern can be formed. In this paper, we introduce the autocorrelation function of channel reuse pattern, the fairness of channel reuse, and the minimum co-channel BS distance to quantitatively examine the channel reuse pattern formed by the IACS-DCA. It is shown that the IACS-DCA can form a CCI-minimized channel reuse pattern in a distributed manner and that it improves the signal-to-interference ratio (SIR) compared to the other channel assignment schemes.

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Abstract-Recently, a frequency-domain block signal detection (FDBD) using maximum likelihood detection (MLD) employing QR decomposition and M-algorithm (QRM-MLD) was proposed for the reception of the single-carrier (SC) signals transmitted over a frequency-selective fading channel. SC-FDBD with QRM-MLD can significantly improve the bit error rate (BER) performance of SC transmission while reducing significantly the computational complexity compared to the MLD. However, its computational complexity is still high. In this paper, we propose a computationally efficient 2-step QRM-MLD SC-FDBD. Compared to conventional QRM-MLD, the number of symbol candidates can be reduced by using the decision made by minimum mean square error based frequency-domain equalization (MMSE-FDE). We evaluate the BER performance achievable by 2-step QRM-MLD and show that it can significantly reduce the computational complexity while keeping the BER performance almost the same as the conventional QRM-MLD.