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Extreme densification is one of the key technologies to get to high data rates in future 5G networks. In this paper, we consider a solar energy harvesting model for small cell base stations (SCBSs). We use the solar energy resource data (http://rredc.nrel.gov/solar/new_data/confrrm/) by the Cooperative Network for Renewable Resource Measurements. The data set includes measurements for solar irradiance, mainly global horizontal irradiance (GHI), along with a set of predictor variables. The data set spans multiple states including Florida, Oregon, Texas, Mississippi, North Carolina, Georgia, New Mexico and West Virginia. Using the data set in North Carolina, we explore the feasibility and reliability of using solar energy to power SCBSs. In this paper, we test the hypothesis that we can accurately predict the availability status of SCBSs, defined as having sufficient power to provide service to end-users, if the GHI value is within a certain range.

Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based symbol detection for MIMO- OFDM systems is presented where RC is realized through the echo state network (ESN). Detailed energy-efficiency analysis is conducted to characterize the energy-efficiency of the introduced symbol detector. To be specific, the transmit power, the circuit power, and the computational power at both transmitter and receiver are jointly considered for the energy-efficiency analysis. The overall system energy-efficiency as well as the receiver energy-efficiency of the introduced RC-based symbol detector are compared with those of the popular linear minimum mean squared error (LMMSE)-based approach. Simulation and numerical results show that the RC-based symbol detector is a ``green'' solution compared to the traditional LMMSE-based method with lower energy consumption per information bit.

While massive multiple-input multiple-output system (MIMO) promises to provide substantial increase in spectral efficiency-per- cell, due to high dimensionality, estimating its channel is considered as one of the major challenges towards extracting all the benefits of such large antenna systems. In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive-MIMO/Full Dimension (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system using estimation of signal parameter via rotational invariance technique (ESPRIT). Furthermore, we analytically characterize the performance of ESPRIT-based DoA estimation in multiuser scenario, and investigate how pilot contamination, intra-cell, and inter-cell interference affect DoA estimation performance.

In 3D massive MIMO (multiple-input multiple-output) systems, especially in time division duplex (TDD) mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. To be specific, we introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel.

Accurate downlink channel state information at the transmitter (CSIT) is essential to utilize the benefit of 3D Massive MIMO/FD-MIMO systems. Conventional approaches to obtain CSIT for FDD MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for 3D Massive MIMO/FD-MIMO systems because of the large dimensionality of the channel matrix. In this paper, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink (UL) direction-of-arrivals (DoAs) and downlink (DL) direction-of-departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming to compare the performance with traditional method in terms of downlink achievable rate that we derived. Simulation results also verifies that, in terms of achievable rate, our proposed method outperform the traditional beamforming method.

In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive- MIMO#x002F;Full Dimension multiple-input multiple-output (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system. Using estimation of signal parameter via rotational invariance technique (ESPRIT), we investigate into the performance of DoA estimation for two possible scenarios- first, for the case where no angular domain overlapping from interference signals exists with the target user's DoAs, and second, for the case where a number of DoAs of the target user's channel are completely overlapped with DoAs from interference signals, to be specific, from pilot contamination.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive-MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the capacity of the system is analytically characterized. It has been shown that optimum precoding matrix for the massive MIMO OFDM system can be constructed from the DoA estimation at the base station.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.

Simulation is presented on the effects of polarization mode dispersion (PMD) as a source of system impairment in high bit-rate fiber-optic transmission systems. A simulation model is introduced that illustrates how the differential group delay (DGD) affects the bit error rate (BER) limiting the system performance. Power penalty due to PMD is determined from both the eye diagram and BER curve and their mutual consistency is verified. As a PMD compensation scheme, in this paper, a polarization controller (PC) is used and the performance of two systems with and without PC is compared. Results show that at a bit rate of 10 Gbps, for a DGD of 80 ps, power penalty can be improved by about 1.25 dB when using a polarization controller in the system.

A novel polarization multiplexed coherent optical OFDM system is proposed and experimentally demonstrated for 100 Gb/s transmission. In this technique, two signals can be transmitted at the same wavelength with orthogonal state of polarization through OFDM transmitter and at the receiver end those signals are received by coherent detector. The implemented model can be treated as a special kind of multiple-input multiple output (MIMO) model which is intrinsically represented by a two-element Jones vector. Though PMD represents a major source of performance degradation in polarization multiplexed systems due to its polarization sensitive detection, the special MIMO system successfully mitigates the effect without any extra compensation system. Performance of the system is evaluated through simulation and different criteria are analyzed with the simulation result.