In this letter, we propose a sequential convolutional residual network, where we first analyze a tangled network architecture using simplified equations and determine the critical point to untangle the complex network architecture. Although the residual network shows good performance, the learning efficiency is not better than expected at deeper layers because the network is excessively intertwined. To solve this problem, we propose a network in which the information is transmitted sequentially. In this network architecture, the neighboring layer output adds the input of the current layer and iteratively passes its result to the next sequential layer. Thus, the proposed network can improve the learning efficiency and performance by successfully mitigating the complexity in deep networks. We show that the proposed network performs well on the Cifar-10 and Cifar-100 datasets. In particular, we prove that the proposed method is superior to the baseline method as the depth increases.

3GPP (3rd Generation Partnership Project) has started to discuss D2D (Device-to-Device)-aided OTDOA (Observed Time Difference Of Arrival) as one of the mobile positioning enhancement techniques for LTE (Long Term Evolution) systems. It is a kind of multi-node based OTDOA which directly receives D2D signals from adjacent multiple UEs (User Equipment) to measure RSTD (Reference Signal's Time Difference). D2D signals provide valuable advantages in terms of OTDOA positioning because it can guarantee more reference nodes and high SNR (Signal-to-Noise Ratio) of PRS (Positioning Reference Signal). Two typical methods for multi-node based OTDOA can be applied to D2D-aided OTDOA. Multiple OTDOA positioning is one of the multi-node based methods that averages multiple results from OTDOA; however, it cannot always guarantee high accuracy due to the non-uniform geometry of UEs. OTDOA positioning based on TSE (Taylor Series Expansion) algorithm may be one of the solutions; however, it has the initial value problem and high computational complexity due to its iterative procedure. Therefore, in this paper, we propose a novel D2D-aided OTDOA positioning method which utilizes UEs not as reference node of OTDOA but as assisting node for RSTD error reduction. The proposed method can reduce RSTD error of eNB based hyperbola by using multiple hyperbola bands. The hyperbola band indicates the possible range in which a hyperbola can occur due to RSTD error. Then, by using principal axes of hyperbolas, we estimate a modified hyperbola from the overlap area of hyperbola bands, which has less RSTD error. We verify that the proposed method can effectively reduce RSTD error and improve positioning performance with lower computational complexity.

In recent 3GPP (3rd Generation Partnership Project) standardization meetings, D2D (Device-to-Device) discovery has been a major issue to support commercial/social services and public safety in disaster environment, and TDM (Time Division Multiplexing) based discovery channel structure is mainly considered to prevent mutual interference between D2D and cellular traffic. In this structure, D2D discovery among the same cell UEs (User Equipment) has no problem because they have the same timing source. However, LTE (Long Term Evolution) assumes an asynchronous network where two adjacent eNBs (evolved Node B) have a symbol-level timing offset. For that reason, asynchronous interference among discovery signals can appear in inter-cell D2D discovery. Therefore, channel re-use scheduling was studied previously in which neighboring cells do not use the same portion of the extended discovery channel and other non-neighboring cells re-use it. However, it still shows interference problems in small cell networks which cause substantial cellular traffic loss. Therefore, in this paper, we propose a novel discovery channel scheduling in which eNBs time-align their discovery channels from each other by sample-level. In the proposed scheme, serving eNB requests cell edge UEs to estimate NTD (Network Time Difference) between serving eNB and neighboring eNB. Then, considering multiple NTDs, eNB adjusts the sample position of its discovery channel based on a novel decision rule. We verify that the proposed scheme can match the discovery performance of a synchronous network with less cellular uplink loss.

This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

D2D (Device-to-Device) communication has received considerable attention in recent years as one of the key technologies for future communication systems. Among the typical D2D communication systems, FlashLinQ (FLQ) adopted single-tone OFDM (Orthogonal Frequency Division Multiplexing) transmission which enables wide-sense discovery and distributed channel-aware link scheduling. Although synchronization based on a CES (Common External Source) is basically assumed in FLQ, a means to support devices when they are unable to use a CES is still necessary. In most OFDM systems, CFO (Carrier Frequency Offset) induces ICI (Inter Channel Interference) which degrades overall system performance drastically. Especially in D2D systems, ICI can be amplified due to different path losses between link and a precise estimation and correction of CFO is very important. Many CFO estimation algorithms based on DA (Data Aided) and NDA (None Data Aided) were proposed for OFDM systems, but there are several constraint conditions on frequency synchronization in D2D systems. Therefore, in this paper, we propose a new NDA-CFO estimation method for OFDM based D2D systems. The proposed method is based on the characteristics of single-tone OFDM signal, and is composed of two estimation stages: initial estimation and feed-back estimation. In initial estimation, the estimation of CFO is obtained by using two correlation results in a symbol. Also, estimation range can be adaptively defined as the distance between the two windows. In feed-back estimation, the distance between the two correlation results is gradually increased by re-using the estimated CFO and the correlation results. Therefore, more precise CFO estimation can be obtained. A numerical analysis and performance evaluation verify that the proposed method has a large estimation range and achieves precise estimation performance compared to the conventional methods.