Research efforts initiated by the EXAT Initiative are described to realize Exabit/s optical communications, utilizing the 3M technologies, i.e. multi-core fiber, multi-mode control and multi-level modulation.

This paper describes the broadband radio access techniques for Universal Mobile Terrestrial Systems (UMTS)/Wideband Code Division Multiple Access (W-CDMA), High-Speed Downlink Packet Access (HSDPA)/High-Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), and LTE-Advanced. Major technical pillars are almost identical regardless of the radio access systems of the respective generations. However, the key techniques that provide distinct performance improvements have changed according to the system requirements in each generation. Hence, in this paper, we focus on the key techniques associated with the system requirements. We also describe the requirements, radio access technology candidates, and challenges toward the future 5G systems.

This paper presents a system for the automatic generation of Galois-field (GF) arithmetic circuits, named the GF Arithmetic Module Generator (GF-AMG). The proposed system employs a graph-based circuit description called the GF Arithmetic Circuit Graph (GF-ACG). First, we present an extension of the GF-ACG to handle GF(pm) (p≥3) arithmetic circuits, which can be efficiently implemented by multiple-valued logic circuits in addition to the conventional binary circuits. We then show the validity of the generation system through the experimental design of GF(pm) multipliers for different p-values. In addition, we evaluate the performance of three types of GF(2m) multipliers and typical GF(pm) multipliers (p≥3) empirically generated by our system. We confirm from the results that the proposed system can generate a variety of GF parallel multipliers, including practical multipliers over GF(pm) having extension degrees greater than 128.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

In this paper, the problem of channel estimation in orthogonal frequency-division multiplexing systems over fast time-varying channel is investigated by using a Basis Expansion Model (BEM). Regarding the effects of the Gibbs phenomenon in the BEM, we propose a new method to alleviate it and reduce the modeling error. Theoretical analysis and detail comparison results show that the proposed BEM method can provide improved modeling error compared with other BEMs such as CE-BEM and GCE-BEM. In addition, instead of using the frequency-domain Kronecker delta structure, a new clustered pilot structure is proposed to enhance the estimation performance further. The new clustered pilot structure can effectively reduce the inter-carrier interference especially in the case of high Doppler spreads.

The development of multi-view video has paved the way for emerging 3D applications. In general multi-view video streaming, video frames for all viewpoints, i.e., cameras, must be transmitted to viewers because the view-switching demands of all viewers are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, frame loss in one viewpoint can induce a collapse in decoding for other viewpoints. To improve loss-resilience, this paper proposes a multi-path based multi-view video transmission scheme. Our scheme encodes video frames into multiple versions that are independent of each other, using inter-view prediction. The scheme then transmits each version using multiple transmission paths. Our scheme makes three contributions: 1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by single-frame loss, and 3) it conceals frame loss by taking video frames from other paths. Evaluations show that our proposed scheme improves video quality by 3 dB, as compared to existing transmission schemes in loss-prone environments.

Detecting signals in a very large multiple-input multiple-output (MIMO) system requires high complexy of implementation. Thus, belief propagation based detection has been studied recently because of its low complexity. When the transmitted signal sequence is encoded using a channel code decodable by a factor-graph-based algorithm, MIMO signal detection and channel decoding can be combined in a single factor graph. In this paper, a low density parity check (LDPC) coded MIMO system is considered, and two types of factor graphs: bipartite and tripartite graphs are compared. The former updates the log-likelihood-ratio (LLR) values at MIMO detection and parity checking simultaneously. On the other hand, the latter performs the updates alternatively. Simulation results show that the tripartite graph achieves faster convergence and slightly better bit error rate performance. In addition, it is confirmed that the LLR damping in LDPC decoding is important for a stable convergence.

In this paper, the recent advances in cooperative distributed antenna transmission (CDAT) are introduced for spatial diversity and multi-user spatial multiplexing in 5G mobile communications network. CDAT is an advanced version of the coordinated multi-point (CoMP) transmission. Space-time block coded transmit diversity (STBC-TD) for spatial diversity and minimum mean square error filtering combined with singular value decomposition (MMSE-SVD) for multi-user spatial multiplexing are described under the presence of co-channel interference from adjacent macro-cells. Blind selected mapping (blind SLM) which requires no side information transmission is introduced in order to suppress the increased peak-to-average signal power ratio (PAPR) of the transmit signals when CDAT is applied. Some computer simulation results are presented to confirm the effectiveness of CDAT techniques.

To drastically increase the splitting ratio of extended-reach (40km span) time- and wavelength-division multiplexed passive optical networks (WDM/TDM-PONs), we modify the gain control scheme of our automatic gain controlled semiconductor optical amplifiers (AGC-SOAs) that were developed to support upstream transmission in long-reach systems. While the original AGC-SOAs are located outside the central office (CO) as repeaters, the new AGC-SOAs are located inside the CO and connected to each branch of an optical splitter in the CO. This arrangement has the potential to greatly reduce the costs of CO-sited equipment as they are shared by many more users if the new gain control scheme works properly even when the input optical powers are low. We develop a prototype and experimentally confirm its effectiveness in increasing the splitting ratio of extended-reach systems to 512.

In the topologies for interconnected nodes, it is desirable to have a low degree and a small diameter. For the same number of nodes, a dual-cube topology has almost half the degree compared to a hypercube while increasing the diameter by just one. Hence, it is a promising topology for interconnection networks of massively parallel systems. We propose here a stochastic fault-tolerant routing algorithm to find a non-faulty path from a source node to a destination node in a dual-cube.

This paper proposes a proportional fair-based joint optimization method for user association and the bandwidth ratio of protected radio resources exclusively used by pico base stations (BSs) for inter-cell interference coordination (ICIC) in heterogeneous networks where low transmission-power pico BSs overlay a high transmission-power macro BS. The proposed method employs an iterative algorithm, in which the user association process for a given bandwidth ratio of protected radio resources and the bandwidth ratio control of protected radio resources for a given user association are applied alternately and repeatedly up to convergence. For user association, we use our previously reported decentralized iterative user association method based on the feedback information of each individual user assisted by a small amount of broadcast information from the respective BSs. Based on numerical results, we show that the proposed method adaptively achieves optimal user association and bandwidth ratio control of protected radio resources, which maximizes the geometric mean user throughput within the macrocell coverage area. The system throughput of the proposed method is compared to that for conventional approaches to show the performance gain.

To overcome the false lock or detection missing problems caused by the multiple peaks of the auto-correlation function (ACF) of Binary Offset Carrier (BOC) modulated signal, an acquisition algorithm based on unit correlation for BOC(n,n) signal is proposed in this paper. The local BOC signal is separated into two unit signals, an odd one and an even one. Then a reconstruction of the unit correlation functions between the unit signals and the received BOC signal is performed and M sections of reconstructed correlation function are accumulated according to the non-coherent method, so that this novel acquisition algorithm can not only eliminate the multiple secondary peaks, but also retain the advantage of the narrow correlation main peak. Simulation results show that the acquisition sensitivity of the proposed algorithm is increased 3dBHz compared with the ASPeCT method, and the computation cost is only 41.46% of the ASPeCT method when M=2.

For network researchers and practitioners, active measurement, in which probe packets are injected into a network, is a powerful tool to measure end-to-end delay. It is, however, suffers the intrusiveness problem, where the load of the probe traffic itself affects the network QoS. In this paper, we first demonstrate that there exists a fundamental accuracy bound of the conventional active measurement of delay. Second, to transcend that bound, we propose INTrusiveness-aware ESTimation (INTEST), an approach that compensates for the delays produced by probe packets in wired networks. Simulations of M/M/1 and MMPP/M/1 show that INTEST enables a more accurate estimation of end-to-end delay than conventional methods. Furthermore, we extend INTEST for multi-hop networks by using timestamps or multi-flow probes.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

Nonnegative matrix factorization (NMF) is one of the most popular machine learning tools for speech enhancement. The supervised NMF-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. However, in many real-world scenarios, it is not always possible for conducting any prior training. The traditional semi-supervised NMF (SNMF) version overcomes this shortcoming while the performance degrades. In this letter, without any prior knowledge of the speech and noise, we present an improved semi-supervised NMF-based speech enhancement algorithm combining techniques of NMF and robust principal component analysis (RPCA). In this approach, fixed speech bases are obtained from the training samples chosen from public dateset offline. The noise samples used for noise bases training, instead of characterizing a priori as usual, can be obtained via RPCA algorithm on the fly. This letter also conducts a study on the assumption whether the time length of the estimated noise samples may have an effect on the performance of the algorithm. Three metrics, including PESQ, SDR and SNR are applied to evaluate the performance of the algorithms by making experiments on TIMIT with 20 noise types at various signal-to-noise ratio levels. Extensive experimental results demonstrate the superiority of the proposed algorithm over the competing speech enhancement algorithm.

Recently, much progress has been made in the study of belief propagation (BP) based signal detection with large-scale factor graphs. When we apply the BP algorithm to equalization in a SISO multipath channel, the corresponding factor graph has many short loops and patterns in an edge connection/strength. Thus, proper convergence may not be achieved. In general, the log-likelihood ratio (LLR) oscillates in ill-converged cases. Therefore, LLR oscillation avoidance is important for BP-based equalization. In this paper, we propose applying node selection (NS) to prevent the LLR from oscillating. The NS extends the loop length virtually by a serial LLR update. Thus, some performance improvement is expected. Simulation results show that the error floor is significantly reduced by NS in the uncoded case and that the NS works very well in the coded case.

Non-contiguous orthogonal frequency-division multiplexing (OFDM) is a promising technique for cognitive radio systems. The secondary users transmit on the selected subcarriers to avoid the frequencies being used by the primary users. However, the out-of-band power (OBP) of the OFDM-modulated tones induces interference to the primary users. Another major drawback of OFDM-based system is their high peak-to-average power ratio (PAPR). In this paper, algorithms are proposed to jointly reduce the OBP and the PAPR for non-contiguous OFDM based on the method of alternating projections onto convex sets. Several OFDM subcarriers are selected to accommodate the adjusting weights for OBP and PAPR reduction. The frequency-domain OFDM symbol is projected onto two convex sets that are defined according to the OBP requirements and the PAPR limits. Each projection iteration solves a convex optimization problem. The projection onto the set constrained by the OBP requirement can be calculated using an iterative algorithm which has low computational complexity. Simulation results show good performance of joint reduction of the OBP and the PAPR. The proposed algorithms converge quickly in a few iterations.

This paper experimentally verifies the potential of higher order space division multiplexing in line-of-sight (LOS) channels for multiuser massive MIMO. We previously proposed an inter-user interference (IUI) cancellation scheme and a simplified user scheduling method for Massive Antenna Systems for Wireless Entrance (MAS-WE). In order to verify the effectiveness of the proposed techniques, channel state information (CSI) for a 1×32 SIMO channel is measured in a real propagation environment with simplified test equipment. Evaluations of the measured CSI data confirm the effectiveness of our proposals; they offer good equal gain transmission (EGT) performance, reduced spatial correlation with enlarged angular gap between users, and quite small channel state fluctuation. Link level simulations elucidate that the simple IUI cancellation method is stable in practical conditions. The degradation in symbol error rate with the measured CSI, relative to that yielded by the output of the theoretical LOS channel model, is insignificant.

Android is one of the most popular mobile device platforms. However, since Android apps can be disassembled easily, attackers inject additional advertisements or malicious codes to the original apps and redistribute them. There are a non-negligible number of such repackaged apps. We generally call those malicious repackaged apps “clones.” However, there are apps that are not clones but are similar to each other. We call such apps “relatives.” In this work, we developed a framework called APPraiser that extracts similar apps and classifies them into clones and relatives from the large dataset. We used the APPraiser framework to study over 1.3 million apps collected from both official and third-party marketplaces. Our extensive analysis revealed the following findings: In the official marketplace, 79% of similar apps were attributed to relatives, while in the third-party marketplace, 50% of similar apps were attributed to clones. The majority of relatives are apps developed by prolific developers in both marketplaces. We also found that in the third-party market, of the clones that were originally published in the official market, 76% of them are malware.

The Möbius cube is a variant of the hypercube. Its advantage is that it can connect the same number of nodes as a hypercube but with almost half the diameter of the hypercube. We propose an algorithm to solve the node-to-node disjoint paths problem in n-Möbius cubes in polynomial-order time of n. We provide a proof of correctness of the algorithm and estimate that the time complexity is O(n2) and the maximum path length is 3n-5.