Joint transmit and receive antenna selection (JTRAS) for transceive spatial modulation (TRSM) is investigated in this paper. A couple of low-complexity and efficient JTRAS algorithms are proposed to improve the reliability of TRSM systems by maximizing the minimum Euclidean distance (ED) among all received signals. Specifically, the QR decomposition based ED-JTRAS achieves near-optimal error performance with a moderate complexity reduction as compared to the optimal ED-JTRAS method. The singular value decomposition based ED-JTRAS achieves sub-optimal error performance with a significant complexity reduction. Simulation results show that the proposed methods remarkably improve the system reliability in both uncorrelated and spatially correlated Rayleigh fading channels, as compared to the conventional norm based JTRAS method.

This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.

Generalized approximate message passing (GAMP) is introduced into distributed compressed sensing (DCS) to reconstruct jointly sparse signals under the mixed support-set model. A GAMP algorithm with known support-set is presented and the matching pursuit generalized approximate message passing (MPGAMP) algorithm is modified. Then, a new joint recovery algorithm, referred to as the joint MPGAMP algorithm, is proposed. It sets up the jointly shared support-set of the signal ensemble with the support exploration ability of matching pursuit and recovers the signals' amplitudes on the support-set with the good reconstruction performance of GAMP. Numerical investigation shows that the joint MPGAMP algorithm provides performance improvements in DCS reconstruction compared to joint orthogonal matching pursuit, joint look ahead orthogonal matching pursuit and regular MPGAMP.

This paper evaluates the throughput of a distributed antenna network (DAN) with multiple mobile terminal scheduling and the usage of joint maximum-likelihood detection (MLD). Mobile terminals are closer to the desired antennas in the DAN which leads to higher throughput and better frequency utilization efficiency. However, when multiple mobile terminal scheduling is applied to the DAN, interference can occur between transmitted signals from antennas. Therefore, in this research, mobile terminal scheduling along with joint MLD is applied to reduce the effects of interference. A system level simulation shows that the usage of joint MLD in a densely packed DAN provides better system throughput regardless of the numbers of mobile terminals and fading channels.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

As an optimal combinatorial object, cyclic perfect Mendelsohn difference family (CPMDF) was introduced by Fuji-Hara and Miao to construct optimal optical orthogonal codes. In this paper, we propose a direct construction of disjoint CPMDFs from the Zeng-Cai-Tang-Yang cyclotomy. Compared with a recent work of Fan, Cai, and Tang, our construction doesn't need to depend on a cyclic difference matrix. Furthermore, strictly optimal frequency-hopping sequences (FHSs) are a kind of optimal FHSs which has optimal Hamming auto-correlation for any correlation window. As an application of our disjoint CPMDFs, we present more flexible combinatorial constructions of strictly optimal FHSs, which interpret the previous construction proposed by Cai, Zhou, Yang, and Tang.

This paper considers a joint channel coding and random number generation from the channel output. Specifically, we want to transmit a message to a receiver reliably and at the same time the receiver extracts pure random bits independent of the channel input. We call this problem as the joint channel coding and intrinsic randomness problem. For general channels, we clarify the trade-off between the coding rate and the random bit rate extracted from the channel output by using the achievable rate region, where both the probability of decoding error and the approximation error of random bits asymptotically vanish. We also reveal the achievable rate regions for stationary memoryless channels, additive channels, symmetric channels, and mixed channels.

This paper applies minimum mean square error (MMSE) interference rejection followed by joint maximum likelihood detection (MLD) to a receiver in a distributed antenna network (DAN). DAN receivers capture not only the desired signals, but also the interference signals from nearby uncoordinated antennas. For the overloaded signal situation, non-linear detection schemes such as joint MLD can be applied to the received signals. However, the amount of metric calculations in joint MLD increases exponentially with the number of signal streams. Therefore, MMSE interference rejection followed by MLD detection is proposed. The proposed scheme reduces the complexity by a factor of 1/2M(NT-1) where NT is the number of interference signals with 2MQAM modulation. The effect of residual interference after the MMSE interference rejection is evaluated. Numerical results obtained through computer simulation and experiment show that the performance of the proposed scheme is about 4.0dB worse at a bit error rate (BER) of 10-3 than that of the joint MLD while its complexity is four times lower for QPSK signal streams. The BER performance degradation can be suppressed to about 2.5dB by adjusting the value of the coefficient in the MMSE matrix.

In this paper, the topology optimization method is first applied to obtain high gain characteristics of dielectric flat lens. The topology optimization method used in this study is based on the gradient method with adjoint variable method. The FDTD method is used as the analysis method of electromagnetic fields. Results are compared with those obtained by using metaheuristic methods GA and PSO. As a result, it is shown that the proposed method can efficiently design a high gain dielectric flat lens in a wide frequency band.

We report good responsivity at the wavelength of 1600nm in a Ge photodetector which had lateral p-i-n structure and butt-joint coupling structure based on conventional normal complementary metal oxide semiconductor processes. We experimentally verified the responsivity of 0.82A/W and 0.71A/W on the best and the worst polarizations, respectively. The butt joint lateral p-i-n structure is found to be polarization independent as compared with vertical ones. Although cut-off frequency was 2.3-2.4GHz at reverse bias 3V, clearly open eye diagram at 10Gbps was obtained with reverse bias over 12V. These results are promising as optical photodetectors to receive long wavelengths downstream signal wavelengths required for next-generation optical access network systems.

High-resolution image and video communication in home networks is highly expected to proliferate with the spread of Wi-Fi devices and the introduction of multiple-input multiple-output (MIMO) systems. This paper proposes a joint transmission and coding scheme for broadcasting high-resolution video streams over multiuser MIMO systems with an eigenbeam-space division multiplexing (E-SDM) technique. Scalable video coding makes it possible to produce the code stream comprised of multiple layers having unequal contribution to image quality. The proposed scheme jointly assigns the data of scalable code streams to subcarriers and spatial streams based on their signal-to-noise ratio (SNR) values in order to transmit visually important data with high reliability. Simulation results show that the proposed scheme surpasses the conventional unequal power allocation (UPA) approach in terms of both peak signal-to-noise ratio (PSNR) of received images and correct decoding probability. PSNR performance of the proposed scheme exceeds 35dB with the probability of over 95% when received SNR is higher than 6dB. The improvement in average PSNR by the proposed scheme compared to the conventional UPA comes up to approx. 20dB at received SNR of 6dB. Furthermore, correct decoding probability reaches 95% when received SNR is greater than 4dB.

Entity descriptions have been exponentially growing in community-generated knowledge databases, such as DBpedia. However, many of those descriptions are not useful for identifying the underlying characteristics of their corresponding entities because semantically redundant facts or triples are included in the descriptions that represent the connections between entities without any semantic properties. Entity summarization is applied to filter out such non-informative triples and meaning-redundant triples and rank the remaining informative facts within the size of the triples for summarization. This study proposes an entity summarization approach based on pre-grouping the entities that share a set of attributes that can be used to characterize the entities we want to summarize. Entities are first grouped according to projected multilingual categories that provide the multi-angled semantics of each entity into a single entity space. Key facts about the entity are then determined through in-group-based rankings. As a result, our proposed approach produced summary information of significantly better quality (p-value =1.52×10-3 and 2.01×10-3 for the top-10 and -5 summaries, respectively) than the state-of-the-art method that requires additional external resources.

This paper presents outdoor field experimental results to clarify the 4-by-4 multiple-input multiple-output (MIMO) throughput performance when applying joint transmission (JT) and distributed MIMO to the 15-GHz frequency band in the downlink of a 5G cellular radio access system. Experimental results for JT in a 100m × 70m large-cell scenario show that throughput improvement of up to 10% is achieved in most of the area and the peak data rate is improved from 2.8Gbps to 3.7Gbps. Based on analysis of the reference signal received power (RSRP) and channel correlation, we find that the RSRP is improved in lower RSRP areas, and that the channel correlation is improved in higher RSRP areas. These improvements contribute to higher throughput performance. The advantage of distributed MIMO and JT are compared in a 20m × 20m small-cell scenario. The throughput improvement of 70% and throughput exceeding 5 Gbps were achieved when applying distributed MIMO due to the improvement in the channel correlation. When applying JT, the RSRP is improved; however the channel correlation is not. As a result, there is no improvement in the throughput performance in the area. Finally, the relationship between the transmission point (TP) allocation and the direction of user equipment (UE) antenna arrangement is investigated. Two TP positions at 90 and 180deg. from each other are shown to be advantageous in terms of the throughput performance with different direction of UE antenna arrangement. Thus, we conclude that JT and distributed MIMO are promising technologies for the 5G radio access system that can compensate for the propagation loss and channel correlation in high frequency bands.

A novel real-valued ESPRIT (RV-ESPRIT) algorithm is proposed to estimate the direction of arrival (DOA) and direction of departure (DOD) for noncircular signals in bistatic MIMO radar. By exploiting the property of signal noncircularity and Euler's formula, a new virtual array data of bistatic MIMO radar, which is twice that of the MIMO virtual array data, is established with real-valued sine and cosine data. Then the receiving/transmitting selective matrices are constructed to obtain the receiving/transmitting rotationally invariant factors. Compared to the existing angle estimation methods, the proposed algorithm has lower computational load. Simulation results confirm the effectiveness of the RV-ESPRIT.

In this paper, we investigate a user and antenna joint selection problem in multi-user large-scale MIMO downlink networks, where a BS with N transmit antennas serves K users, and N is much larger than K. The BS activates only S(S≤N) antennas for data transmission to reduce hardware cost and computation complexity, and selects the set of users to which data is to be transmitted by maximizing the sum-rate. The optimal user and antenna joint selection scheme based on exhaustive search causes considerable computation complexity. Thus, we propose a new joint selection algorithm with low complexity and analyze the performance of the proposed scheme in terms of sum-rate and complexity. When S=7, N=10, K=5, and SNR=10dB, the sum-rate of the proposed scheme is 5.1% lower than that of the optimal scheme, while the computation complexity of the proposed scheme is reduced by 99.0% compared to that of the optimal scheme.

Non-orthogonal multiple access (NOMA) makes multiple mobile users share the same frequency band. In a conventional NOMA scheme, a user pair that can be assigned to the same frequency resource is limited, which reduces the amount of capacity improvement possible. This is because a far user demodulates a signal without canceling an underlaid signal for a near user. In addition, semi-orthogonal multiple access (SOMA) modulation has been proposed. This modulation scheme helps to reduce scheduling complexity and demodulation complexity. In this paper, a joint detection scheme is applied to a far user as well as a near user in a NOMA downlink. The joint detection in the far user leads to a more number of user pairs that can be assigned to the same frequency resource through proportional fair scheduling. The total system throughput performance with the joint detection is evaluated with multi-cell system level simulation. Numerical results show that the joint detection in the original NOMA system increases the system throughput more effectively than that with SOMA modulation.

In this letter, we explore joint optimization of perceptual gain function and deep neural networks (DNNs) for a single-channel speech enhancement task. A DNN architecture is proposed which incorporates the masking properties of the human auditory system to make the residual noise inaudible. This new DNN architecture directly trains a perceptual gain function which is used to estimate the magnitude spectrum of clean speech from noisy speech features. Experimental results demonstrate that the proposed speech enhancement approach can achieve significant improvements over the baselines when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

Non-orthogonal multiple access (NOMA) enables multiple mobile devices to share the same frequency band. In a conventional NOMA scheme, the receiver of a far user detects its desired signal without canceling the signal for a near user. However, the signal for the near user acts as interference and degrades the accuracy of likelihood values for the far user. In this paper, a joint maximum likelihood detection scheme for the far user of the NOMA downlink is proposed. The proposed scheme takes the interference signal into account in calculating the likelihood values. Numerical results obtained through computer simulation show that the proposed scheme improves the performance by from 0.2dB to 3.1dB for power allocation coefficients of 0.2 to 0.4 at a bit error rate (BER) of 10-2 relative to the conventional scheme.

Considering that existing clutter cancellation methods process information either in the time domain or in the spatial domain, this paper proposes a new clutter cancellation method that utilizes joint multi-domain information for passive radar. Assuming that there is a receiving array at the surveillance channel, firstly we propose a multi-domain information clutter cancellation model by constructing a time domain weighted matrix and a spatial weighted vector. Secondly the weighted matrix and vector can be updated adaptively utilizing the constant modulus constraint. Finally the weighted matrix is derived from the principle of optimal filtering and the recursion formula of weighted vector is obtained utilizing the Gauss-Newton method. Making use of the information in both time and spatial domain, the proposed method attenuates the noise and residual clutter whose directions are different from that of the target echo. Simulation results prove that the proposed method has higher clutter attenuation (CA) compared with the traditional methods in the low signal to noise ratio condition, and it also improves the detection performance of weak targets.

In this paper we discuss approximation algorithms for the ELEMENT-DISJOINT STEINER TREE PACKING problem (Element-STP for short). For a graph G=(V,E) and a subset of nodes T⊆V, called terminal nodes, a Steiner tree is a connected, acyclic subgraph that contains all the terminal nodes in T. The goal of Element-STP is to find as many element-disjoint Steiner trees as possible. Element-STP is known to be APX-hard even for |T|=3 [1]. It is also known that Element-STP is NP-hard to approximate within a factor of Ω(log |V|) [3] and there is an O(log |V|)-approximation algorithm for Element-STP [2],[4]. In this paper, we provide a $lceil rac{|T|}{2} ceil$-approximation algorithm for Element-STP on graphs with |T| terminal nodes. Furthermore, we show that the approximation ratio of 3 for Element-STP on graphs with five terminal nodes can be improved to 2.