This paper proposes a novel dynamic channel assignment scheme named interference-aware dynamic channel assignment (IA-DCA) for the downlink of enterprise small-cell networks (ESNs) that employ orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In ESNs, a lot of small-cell access points (SAPs) are densely deployed in a building and thus small-cell user equipments (SUEs) have more serious co-tier interference from neighbor SAPs than the conventional small-cell network. Therefore, in the proposed IA-DCA scheme, a local gateway (LGW) dynamically assigns different numbers of subchannel groups to SUEs through their serving SAPs according to the given traffic load and interference information. Through simulation results, we show that the proposed IA-DCA scheme outperforms other dynamic channel assignment schemes based on graph coloring algorithm in terms of the mean SUE capacity, fairness, and mean SAP channel utilization.

Multiple input multiple output with orthogonal frequency division multiplexing (MIMO-OFDM) is used in various parts of wireless communication systems. Because the MIMO-OFDM system simultaneously transmits parallel data streams and each receive antenna receives all data streams at one time, the detection ability of the receiver is very important. Among the detection schemes suitable for OFDM, maximum likelihood (ML) detection has optimal performance, but its complexity is so high that it is infeasible. Linear detection schemes such as zero-forcing (ZF) and minimum mean square error (MMSE) have low complexity, but also low performance. Among non-linear detection schemes, the near-ML detection which is the sphere detection (SD) or the QR decomposition with M algorithm (QRD-M) also has optimal performance but the complexity of SD and QRD-M detection is also too high. Other non-linear detection schemes like successive interference cancellation (SIC) detection have low complexity. However, the performance of SIC detection is lower than other non-linear detection schemes. In this paper, selectively iterative detection is proposed for MIMO-OFDM system; it offers low complexity and good performance.

We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method.

Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.

This paper intends to reduce duration times in typical collective communications. We introduce logical addressing system apart from the physical one and, by rearranging the logical node addresses properly, we intend to reduce communication overheads so that ideal communication is performed. One of the key issues is rearrangement of the logical addressing system. We introduce genetic algorithm (GA) as meta-heuristic solution as well as the random search strategy. Our GA-based method achieves at most 2.50 times speedup in three-traffic-pattern cases.

In this paper, the analog code modulation characteristics of distributed-based transversal filters (DTFs) suitable for use in spectrally encoded CDMA systems are presented. The DTF is verified as an appropriate method to use in high-speed CDMA systems as opposed to previously proposed methods, which are intended for Direct Sequence (DS) CDMA systems. The large degree of freedom of DTF design permits controlling the filter pulse response to generate well specified temporal phase-coded signals. A decoder structure that performs bipolar detection of user subbands giving rise to a Spectral-Amplitude Encoded CDMA system is considered. Practical implementations require truncating the spreading signals by a time window of duration equal to the span time of the tapped delay line. Filter functions are chosen to demodulate the matched channel and achieve improved user interference rejection avoiding the need for transversal filters featuring a large number of taps. As a proof-of-concept of the electronic SAE scheme, practical circuit designs are developed at low speeds (3-dB point at 1 GHz) demonstrating the viability of the proposal.

Latent variable models for relational data enable us to extract the co-cluster structure underlying observed relational data. The Infinite Relational Model (IRM) is a well-known relational model for discovering co-cluster structures with an unknown number of clusters. The IRM and several related models commonly assume that the link probability between two objects depends only on their cluster assignment. However, relational models based on this assumption often lead us to extract many non-informative and unexpected clusters. This is because the cluster structures underlying real-world relationships are often blurred by biases of individual objects. To overcome this problem, we propose a multi-layered framework, which extracts a clear de-blurred co-cluster structure in the presence of object biases. Then, we propose the Multi-Layered Infinite Relational Model (MLIRM) which is a special instance of the proposed framework incorporating the IRM as a co-clustering model. Furthermore, we reveal that some relational models can be regarded as special cases of the MLIRM. We derive an efficient collapsed Gibbs sampler to perform posterior inference for the MLIRM. Experiments conducted using real-world datasets have confirmed that the proposed model successfully extracts clear and interpretable cluster structures from real-world relational data.

Depth image based rendering (DIBR), which is utilized to render virtual views with a color image and the corresponding depth map, is one of the key procedures in the 2D to 3D conversion process. However, some troubling problems, such as depth edge misalignment, disocclusion occurrences and cracks at resampling, still exist in current DIBR systems. To solve these problems, in this paper, we present a robust depth image based rendering scheme for stereoscopic view synthesis. The cores of the proposed scheme are two depth map filters which share a common domain transform based filtering framework. As a first step, a filter of this framework is carried out to realize texture-depth boundary alignments and directional disocclusion reduction smoothing simultaneously. Then after depth map 3D warping, another adaptive filter is used on the warped depth maps with delivered scene gradient structures to further diminish the remaining cracks and noises. Finally, with the optimized depth map of the virtual view, backward texture warping is adopted to retrieve the final texture virtual view. The proposed scheme enables to yield visually satisfactory results for high quality 2D to 3D conversion. Experimental results demonstrate the excellent performances of the proposed approach.

This paper proposes a block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling. A BPBE image can be obtained through four processes, positional scrambling, block rotation/flip, negative-positive transformation, and color component shuffling, after dividing the original image into multiple blocks. The proposed scheme scrambles the R, G, and B components independently in positional scrambling, block rotation/flip, and negative-positive transformation, by assigning different keys to each color component. The conventional scheme considers the compression efficiency using JPEG and JPEG 2000, which need a color conversion before the compression process by default. Therefore, the conventional scheme scrambles the color components identically in each process. In contrast, the proposed scheme takes into account the RGB-based compression, such as JPEG-LS, and thus can increase the extent of the scrambling. The resilience against jigsaw puzzle solver (JPS) can consequently be increased owing to the wider color distribution of the BPBE image. Additionally, the key space for resilience against brute-force attacks has also been expanded exponentially. Furthermore, the proposed scheme can maintain the JPEG-LS compression efficiency compared to the conventional scheme. We confirm the effectiveness of the proposed scheme by experiments and analyses.

In this study, spatially coupled low-density parity-check (SC-LDPC) codes on the two-dimensional array erasure (2DAE) channel are devised, including a method for generating new SC-LDPC codes with a restriction on the check node constraint. A density evolution analysis confirms the improvement in the threshold of the proposed two-dimensional SC-LDPC code ensembles over the one-dimensional SC-LDPC code ensembles. We show that the BP threshold of the proposed codes can approach the corresponding maximum a posteriori (MAP) threshold of the original residual graph on the 2DAE channel. Moreover, we show that the rates of the residual graph of the two-dimensional LDPC block code ensemble are smaller than those of the one-dimensional LDPC block code ensemble. In other words, a high performance can be obtained by choosing the two-dimensional SC-LDPC codes.

Lasso regression based on the L1 regularization is one of the most popular sparse estimation methods. It is often required to set appropriately in advance the regularization parameter that determines the degree of regularization. Although the empirical Bayes approach provides an effective method to estimate the regularization parameter, its solution has yet to be fully investigated in the lasso regression model. In this study, we analyze the empirical Bayes estimator of the one-parameter model of lasso regression and show its uniqueness and its properties. Furthermore, we compare this estimator with that of the variational approximation, and its accuracy is evaluated.

The present paper introduces a novel type of structured ternary sequences having a zero-correlation zone (zcz) for both periodic and aperiodic correlation functions. The cross-correlation function and the side lobe of the auto-correlation function of the proposed sequence set are zero for phase shifts within the zcz. The proposed zcz sequence set can be generated from an arbitrary pair of an Hadamard matrix of order lh and a binary/ternary perfect sequence of length lp. The sequence set of order 0 is identical to the r-th row of the Hadamard matrix. For m ≥ 0, the sequence set of order (m+1) is constructed from the sequence set of order m by sequence concatenation and interleaving. The sequence set has lp subsets of size 2lh. The periodic correlation function and the aperiodic correlation function of the proposed sequence set have a zcz from -(2m+1-1) to 2m+1-1. The periodic correlation function and the aperiodic correlation function of the sequences of the i-th subset and k-th subset have a zcz from -2m+2-(lh+1)((j-k) mod lp) to -2m+2-(lh+1)((j-k) mod lp). The proposed sequence is suitable for a heterogeneous wireless network, which is one of the candidates for the fifth-generation mobile networks.

This paper describes a design methodology for process variation aware D-Flip-Flop (DFF) using regression analysis. We propose to use a regression analysis to model the worst-case delay characteristics of a DFF under process variation. We utilize the regression equation for transistor width tuning of the DFF to improve its worst-case delay performance. Regression analysis can not only identify the performance-critical transistors inside the DFF, but also shows these impacts on DFF delay performance in quantitative form. Proposed design methodology is verified using Monte-Carlo simulation. The result shows the proposed method achieves to design a DFF which has similar or better delay characteristics in comparison with the DFF designed by an experienced cell designer.

Several types of capacitive crosstalk avoidance codes have been devised in order to prevent capacitive crosstalk in on-chip buses. These codes are designed to prohibit transition patterns prone to capacitive crosstalk from any two consecutive words transmitted to on-chip buses. The present paper provides a rigorous analysis of the asymptotic rate for (p,q)-transition free word sequences under the assumption that coding is based on a stateful encoder and a stateless decoder. Here, p and q represent k-bit transition patterns that should not appear in any two consecutive words at the same adjacent k-bit positions. The maximum rate for the sequences is proven to be equal to the subgraph domatic number of the (p,q)-transition free graph. Based on the theoretical results for the subgraph domatic partition problem, lower and upper bounds on the asymptotic rate are derived. We also show that the asymptotic rate 0.8325 is achievable for p=01 and q=10 transition free word sequences.

In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

Excitation of Extremely Low Frequency (ELF)/Very Low Frequency (VLF) from ionosphere,which is artificial modulated by High Frequency (HF) waves can provide a way of antenna generation for deep submarine communication. In this paper, based on plasma energy conservation equation, the theoretical model of amplitude modulation HF pump heating low ionosphere for ELF/VLF generation is established. The linear frequency modulation technique of up-chirp and down-chirp have good self-correlation and cross-correlation, by which information can be transmitted by up-chirp and down-chirp. Thus, the linear frequency modulation technique can be applied to the ionosphere ELF/VLF communication. Based on this, a Chirp-BOK (Binary Orthogonal Keying) communication scheme is proposed. Indeed the Chirp-BOK amplitude and power modulation function are designed by combining the linear frequency modulation technique with the square wave amplitude modulation technique. The simulation results show in the condition that the ionosphere is heated by the Chirp-BOK power modulation HF waves, the temperature of ionospheric electronic and the variations of conductivity have obvious frequency modulation characteristics which are the same as that of power modulation, so does the variation of ionospheric current. Thus, when the ionosphere is heated by Chirp-BOK power modulation HF waves, the up-chirp (symbol ‘0’) and down-chirp (symbol ‘1’) ELF/VLF signals can be generated.

Let X, Y be two correlated discrete random variables. We consider an estimation of X from encoded data φ(Y) of Y by some encoder function φ(Y). We derive an inequality describing a relation of the correct probability of estimation and the mutual information between X and φ(Y). This inequality may be useful for the secure analysis of crypto system when we use the success probability of estimating secret data as a security criterion. It also provides an intuitive meaning of the secrecy exponent in the strong secrecy criterion.

We propose an approach to allocate bandwidth for a satellite communications (SATCOM) system that includes the recent high-throughput satellite (HTS) with frequency flexibility. To efficiently operate the system, we manage the limited bandwidth resources available for SATCOM by employing a control method that allows the allocated bandwidths to exceed the communication demand of user terminals per HTS beam. To this end, we consider bandwidth allocation for SATCOM as an optimal control problem. Then, assuming that the model of communication requests is available, we propose an optimal control method by combining model predictive control and sparse optimization. The resulting control method enables the efficient use of the limited bandwidth and reduces the bandwidth loss and number of control actions for the HTS compared to a setup with conventional frequency allocation and no frequency flexibility. Furthermore, the proposed method allows to allocate bandwidth depending on various control objectives and beam priorities by tuning the corresponding weighting matrices. These findings were verified through numerical simulations by using a simple time variation model of the communication requests and predicted aircraft communication demand obtained from the analysis of actual flight tracking data.

In 1973, Arimoto proved the strong converse theorem for the discrete memoryless channels stating that when transmission rate R is above channel capacity C, the error probability of decoding goes to one as the block length n of code word tends to infinity. He proved the theorem by deriving the exponent function of error probability of correct decoding that is positive if and only if R > C. Subsequently, in 1979, Dueck and Körner determined the optimal exponent of correct decoding. Recently the author determined the optimal exponent on the correct probability of decoding have the form similar to that of Dueck and Körner determined. In this paper we give a rigorous proof of the equivalence of the above exponet function of Dueck and Körner to a exponent function which can be regarded as an extention of Arimoto's bound to the case with the cost constraint on the channel input.

Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.