This paper proposes overloaded MIMO spatial multiplexing that can increase the number of spatially multiplexed signal streams despite of the number of antennas on a terminal and that on a receiver. We propose extension of the channel matrix for the spatial multiplexing to achieve the superb multiplexing performance. Precoding based on the extended channel matrix plays a crucial role in carrying out such spatial multiplexing. We consider three types of QR-decomposition techniques for the proposed spatial multiplexing to improve the transmission performance. The transmission performance of the proposed spatial multiplexing is evaluated by computer simulation. The simulation reveals that the proposed overloaded MIMO spatial multiplexing can implement 6 stream-spatial multiplexing in a 2×2 MIMO system, i.e., the overloading ratio of 3.0. The superior transmission performance is achieved by the proposed overloaded MIMO spatial multiplexing with one of the QR-decomposition techniques.

Federated cloud, as a promising technology, can improve the computing capacity for autonomous driving in the vehicle-road-cloud collaborative system. However, the allocation of federated clouds should consider the environmental changes based on the real-time impact of vehicle terminal location. To improve computational efficiency while ensuring the effectiveness of federated clouds, this paper proposes a one-sided matching reverse auction based on the federated clouds (OSFC) method for scheduling autonomous driving sensors in a vehicle-road-cloud collaborative environment. This method dynamically allocates communication resources according to the actual situation of the vehicle terminals in real time. Numerical simulations show that our proposed OSFC method significantly improves computational efficiency while ensuring the effectiveness of federated clouds compared with state-of-the-art work.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) effectively provides a high spectral efficiency and coding gain for digital coherent systems over additive white Gaussian noise (AWGN) and optical fiber channels. We previously proposed combining probabilistic amplitude shaping (PAS) with BICM-ID to further improve the system performance. However, the BICM-ID performance depends on the binary labeling scheme used for the constellation points. In this study, we evaluated the effect of binary labeling schemes on the performance of the PAS with BICM-ID system. Numerical simulations showed that the PAS with BICM-ID system employing a suitable binary labeling scheme offers a significant coding gain over both the AWGN and optical fiber channels. The system is also robust against performance degradation caused by the optical Kerr effect in the optical fiber channel. We used an extrinsic information transfer (EXIT) chart to analyze the suitability of binary labeling schemes and the effect of bit interleavers. The results showed that a binary labeling scheme is suitable if the slope of the demodulator’s EXIT curve is close to the slope of the decoder’s EXIT curve. The EXIT chart analysis also showed that inserting bit interleavers mitigates the performance degradation during iterative decoding. In addition, we used bitwise mutual information to evaluate the SNR penalty due to shaping gap and coding gap, and coding gain offered by iterative decoding of BICM-ID.

Four-wave mixing (FWM) is a crucial impairment factor in optical wavelength-division-multiplexing (WDM) transmission systems over dispersion-shifted fibers. This paper presents an FWM suppression scheme that places dispersive elements (DEs) such as dispersion compensation fibers at optically repeating points in transmission lines. In a DE, the relative phase of the transmitted signal lights and the FWM light generated in the previous spans is shifted. Consequently, the FWM lights generated in each span are summed in random phases and the total FWM power at the end of the transmission lines is reduced from that in straight transmission lines with no DEs. We conduct proof-of-principle experiments to confirm the mechanism of the FWM reduction. Calculation for evaluating the FWM reduction ratio in a WDM transmission system is also presented.

Constrained Application Protocol (CoAP) is a popular UDP based data transfer protocol designed for constrained nodes in lossy networks. Congestion control method is essentially required in such environment to ensure proper data transfer. CoAP offers a default congestion control technique based on binary exponential backoff (BEB) where the default method calculates retransmission time out (RTO) irrespective of Round Trip Time (RTT). CoAP simple congestion control/advanced (COCOA) is a standard alternative algorithm. COCOA computes RTO using exponential weighted moving average (EWMA) based on type of RTT; strong RTT or weak RTT. The constant weight values used in COCOA delays the variability of RTO. This delay in converging the RTO can cause QoS problems in many IoT applications. In this paper, we propose a new method called Flexi COCOA to accomplish a flexible weight based RTO computation for strong RTT samples. We show that Flexi COCOA is more network sensitive than COCOA. Specifically, the variable weight focuses on deriving better RTO and utilizing the resources more. Flexi COCOA is implemented and validated against COCOA using the Cooja simulator in Contiki OS. We carried out extensive simulations using different topologies, packet sending rates and packet error rates. Our results show that Flexi COCOA outshines COCOA and can improve QoS of IoT monitoring applications.

A software-defined network (SDN) is a network that the centralized SDN controller controls multiple SDN switches. Load-balancing platforms can realize the distribution of the load of the switches between multiple controllers. The platforms allow controller processing capacity to be used efficiently. When the assignment between switches and controllers and the controller placement are changed, migration blackout time that the controller temporarily stops processing messages can occur. The migration blackout time can result in failure to meet delay requirements between switches and controllers. This paper proposes a model that determines the controller assignment and placement while minimizing the migration blackout time with the load-balancing platform. The proposed model can be used when the controllers in the network are overloaded and the controller assignment and placement need to be changed. We formulate the proposed model as a mixed-integer second-order cone programming problem. We develop a migration procedure used in the proposed model. In the procedure, each switch can be controlled by multiple controllers with a load-balancing platform. The load-balancing platform allows status messages sent from a switch to be sent to multiple controllers. This allows status messages sent from the switches to be processed in order and the migration blackout can be avoided. The proposed model is compared with a baseline model based on the previous works. In the baseline model, the migration blackout time always occurs when the controller assignment and placement are changed. Numerical results show that the migration blackout time in the proposed model becomes smaller than that in the baseline model. The results also show that the number of controllers placed in the proposed model is smaller than that in the baseline model.

In cloud computing environments with virtual machines (VMs), we propose a VM placement (VMP) method based on traffic estimation to balance loads due to traffic volumes within physical hosts (PHs) and passing through physical network interface cards (NICs). We refer to a VM or a NIC in a cloud environment as node, and define a flow as a pair of nodes. To balance loads for both PHs and NICs, it is necessary to measure flow traffic volumes because each VM may connect to other VMs in different PHs. However, this is not a cost-effective way to measure flow traffic volumes because the number of flows increases with O(N²) for the number N of nodes. To solve this problem, we propose a VMP method using a compressed sensing (CS)-based traffic estimator. In the proposed method, the relationship between flow traffic volumes and node traffic volumes is formulated by a system of underdetermined linear equations. The flow traffic volumes are estimated with CS from the measured node traffic volumes. From the estimated flow traffic volumes, each VM is assigned to the optimal host for load balancing by solving a mixed-integer optimization problem.

To shorten the distance between base stations (BSs) and user terminals, next-generation mobile communications (6G) plans to install large numbers of remote antenna units (RAUs) on traffic signals and street lights and connect these RAUs to base band units (BBUs) on buildings using terahertz (THz) band fronthaul radio lines capable of data rates that exceed 100 Gbit/s. However, when THz band fronthaul wireless circuits are densely deployed in urban areas, the challenge is to maintain line-of-sight (LOS) between RAUs and BBUs and prevent interference between fronthaul wireless links. In this study, the three-dimensional (3D) radiation pattern of a 300-GHz-band high-gain antenna was measured using the near-field-to-far-field (NF-FF) conversion method, and the accuracy was compared with the far-field measurement results. Moreover, an algorithm for automatically deploying a 300-GHz-band wireless fronthaul link is proposed, which can be used to position BBUs in locations where one BBU can be connected to as many RAUs as possible. Propagation simulations for fronthaul wireless links placed by the automatic deployment algorithm, using the measured 3D radiation patterns from high-gain antennas, show no interference between the fronthaul wireless links.

Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.

The effects of site diversity techniques on Ku-band rain attenuation are investigated using two kinds of simultaneous BS (Broadcasting Satellite) signal observations: one was conducted among Osaka Electro-Communication University (OECU) in Neyagawa, Kyoto University in Uji, and Shigaraki MU Observatory in Koka for the past ten years, and the other was conducted among the headquarter of OECU in Neyagawa and their other premises in Shijonawate and Moriguchi for the past seven years, respectively. The site diversity effects among these sites with horizontal separations of 3-50 km are found to be largely affected by the passage direction of rain areas characterized by each rain type, such as warm, cold, and stationary fronts or typhoon and shower. The performance of the site diversity primarily depends on the effective distance between the sites projected to the rain area motions. The unavailable time percentages are theoretically shown to be reduced down to about 61-73% of the ITU-R predictions by choosing a pair of the sites aligned closest to the rain area motion in the distance of 3-50 km. Then, we propose three kinds of novel site diversity methods that choose the pair of sites based on such as rain type, rain front motion, or rain area motion at each rainfall event, respectively. As a result, the first method, which statistically accumulates the average passage directions of each rain type from long-term observations, is even useful for practical operations of the site diversity, because unavailable time percentages are reduced down to about 75-85% compared with the theoretical limit of about 61-73%. Also, the third method based on the rain area motion directly obtained from the three-site observations yields the reduction in unavailable time percentages close to this theoretical limit.

The combination of peak-to-average power ratio (PAPR) reduction and predistortion (PD) techniques effectively reduces the nonlinear distortion of a transmission signal caused by power amplification and improves power efficiency. In this paper, assuming downlink amplify-and-forward (AF)-type relaying of multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) signals, we propose a joint method that combines a PD technique with our previously reported PAPR reduction method utilizing the null space of a MIMO channel. In the proposed method, the reported PAPR reduction method reduces the PAPR at a relay station (RS) as well as that at a base station (BS) by using only signal processing at the BS. The PD process at the BS and RS further reduces the nonlinear distortion caused by nonlinear power amplification. Computer simulation results show that the proposed method enhances the effectiveness of PD at the BS and RS and achieves further coverage enhancement compared to conventional methods.

Synthetic aperture radar (SAR) image generation is crucial to SAR image interpretation when sufficient image samples are unavailable. Against this background, a method for SAR image generation of three-dimensional (3D) target is proposed in this paper. Specifically, this method contains three steps. Firstly, according to the system parameters, the echo signal in the two-dimensional (2D) time domain is generated, based on which 2D Fast Fourier Transform (2DFFT) is performed. Secondly, the hybrid moments (MoM)-large element physical optics (LEPO) method is used to calculate the scattering characteristics with the certain frequency points and incident angles according to the system parameters. Finally, range Doppler algorithm (RDA) is adopted to process the signal in the 2D-frequency domain with radar cross section (RCS) exported from electromagnetic calculations. These procedures combine RCS computations by FKEO solver and RDA to simulate raw echo signal and then generate SAR image samples for different squint angles and targets with reduced computational load, laying foundations for transmit waveform design, SAR image interpretation and other SAR related work.