We present an entirely novel concept of retrieving social media data, called sigSocial. It integrates social media data of various sources, using a semantic classifier. Nowadays, people use multiple social media simultaneously, acquiring information with ease. However, accessing numerous services to reach different channels is bothersome. Also, the volume of information one can process is limited. Our aim is to reduce this burden, providing easiness and efficiency. In other words, we attempt to build a single service that integrates information from various platforms. The application has three main features. First, it enables users to explore multiple social media without accessing them separately. Second, it organizes information retrieved from social medias into well-defined classes. Finally, it works as a stand-alone application, the mechanism of which is internal to the device, not relying on any external servers or networks. This method respects user privacy, which has recently gained much attention.

In this paper, we propose a minimum-mean-squared-error (MMSE)-based lattice-reduction (LR)-aided fixed-complexity sphere decoder (FSD) for low-complexity near-maximum-likelihood (near-ML) multiple-input multiple-output (MIMO) detection. In order for the FSD to achieve optimal performance, the number of full expansion (FE) stages should be sufficient, which is the major cause of the increase in the computational complexity when either a large signal constellation or a large number of antennas are adopted. However, the proposed algorithm maintains the near-ML performance with the aid of the MMSE-based LR algorithm while reducing the number of FE stages. Although there exists the increase in the computational complexity for the application of the additional processing elements, the decrease in the number of FE stages results in the lower computational complexity of the overall algorithm. The numerical analysis demonstrates that there is a considerable decrease in the computational complexity while the performance degradation is negligible, compared to the optimal FSD.

In this paper, we propose a high performance multiple-input and multiple-output (MIMO) detection algorithm for downlink multiuser MIMO (DL MU-MIMO) with practical errors in IEEE 802.11ac systems. The DL MU-MIMO system using the precoding matrix, which is generated through imperfect channel state information caused by channel estimation error and channel feedback quantization error, has severe performance degradation since each station (STA) receives the desired signal for the STA as well as the interference signal for different STAs. Therefore, the proposed detection algorithm is developed by considering both the desired signal and the interference signal. Using the characteristics of the interference signal, the proposed detection algorithm is performed by additionally detecting several interference symbols. The proposed algorithm has better performance than the conventional algorithm considering only the desired signal. Simulation results show that the coded bit error rate performance of the proposed algorithm was improved in all modulation and coding scheme cases, compared to the conventional algorithm.