Wensheng Zhang

Multi-Frequency mmWave Massive MIMO Channel Measurements and Characterization for 5G Wireless Communication Systems

Most millimeter wave (mmWave) channel measurements are conducted with different configurations, which may have large impacts on propagation channel characteristics. In addition, the comparison of different mmWave bands is scarce. Moreover, mmWave massive multiple-input multiple-output (MIMO) channel measurements are absent, and new propagation properties caused by large antenna arrays have rarely been studied yet. In this paper, we carry out mmWave massive MIMO channel measurements at 11-, 16-, 28-, and 38-GHz bands in indoor environments. The space-alternating generalized expectation-maximization algorithm is applied to process the measurement data. Important statistical properties, such as average power delay profile, power azimuth profile, power elevation profile, root mean square delay spread, azimuth angular spread, elevation angular spread, and their cumulative distribution functions and correlation properties, are obtained and compared for different bands. New massive MIMO propagation properties, such as spherical wavefront, cluster birth-death, and non-stationarity over the antenna array, are validated for the four mmWave bands by investigating the variations of channel parameters. Two channel models are used to verify the measurements. The results indicate that massive MIMO effects should be fully characterized for mmWave massive MIMO systems.

Energy–Spectral Efficiency Tradeoff in Cognitive Radio Networks

In this paper, we propose a general framework to evaluate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in cognitive radio networks (CRNs). The proposed framework is discussed in three typical CRN paradigms: underlay CRNs (UCRNs), overlay CRNs (OCRNs), and interweave CRNs (ICRNs). The EE-SE relation for three CRNs is expressed in the closed-form formulation, in which the optimal (suboptimal) EE solution as the function of SE is deduced with the corresponding limits. The theoretical analysis and numerical results indicate that the EE-SE relation in CRNs is not contrary, i.e., an optimal EE-SE tradeoff can be achieved. The proposed framework provides a useful guidance in the design of practical green CRNs.

Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks

In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement.

Exact Distributions of Finite Random Matrices and Their Applications to Spectrum Sensing.

The exact and simple distributions of finite random matrix theory (FRMT) are critically important for cognitive radio networks (CRNs). In this paper, we unify some existing distributions of the FRMT with the proposed coefficient matrices (vectors) and represent the distributions with the coefficient-based formulations. A coefficient reuse mechanism is studied, i.e., the same coefficient matrices (vectors) can be exploited to formulate different distributions. For instance, the same coefficient matrices can be used by the largest eigenvalue (LE) and the scaled largest eigenvalue (SLE); the same coefficient vectors can be used by the smallest eigenvalue (SE) and the Demmel condition number (DCN). A new and simple cumulative distribution function (CDF) of the DCN is also deduced. In particular, the dimension boundary between the infinite random matrix theory (IRMT) and the FRMT is initially defined. The dimension boundary provides a theoretical way to divide random matrices into infinite random matrices and finite random matrices. The FRMT-based spectrum sensing (SS) schemes are studied for CRNs. The SLE-based scheme can be considered as an asymptotically-optimal SS scheme when the dimension K is larger than two. Moreover, the standard condition number (SCN)-based scheme achieves the same sensing performance as the SLE-based scheme for dual covariance matrix K=2. The simulation results verify that the coefficient-based distributions can fit the empirical results very well, and the FRMT-based schemes outperform the IRMT-based schemes and the conventional SS schemes.

Comparison of Propagation Channel Characteristics for Multiple Millimeter Wave Bands

Millimeter wave (mmWave) communication has been a key technology for the fifth generation (5G) wireless communications. There have been various mmWave channel measurements. However, many measurements in the literature are conducted with different configurations, which may have large impacts on the propagation channel characteristics, and make the comparison of propagation channel characteristics for different mmWave bands impossible. In this paper, we carry out channel measurements at 11, 16, 28, and 38 GHz bands in an indoor environment using a vector network analyzer (VNA). The space-alternating generalized expectation-maximization (SAGE) algorithm is used to obtain the multipath component (MPC) parameters including three dimensional (3D) angular domain information. The propagation characteristics like average power delay profile (APDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS) are shown and compared for the four frequency bands. The results show similar properties for different bands and indicate the possibility of the derivation of a unified channel model framework for 10-40 GHz bands.

Multi-frequency millimeter wave massive MIMO channel measurements and analysis

Massive multiple-input multiple-output (MIMO) technology and millimeter wave (mmWave) communication are key technologies for the fifth generation (SG) wireless communications. The combination of mmWave and massive MIMO has the potential to dramatically improve wireless access and throughput performance. Such systems benefit from large available signal bandwidths and small antenna form factor. In the literature, most of the massive MIMO channel measurements are carried out at sub-6 GHz frequency bands, while the effects caused by large antenna arrays at mmWave bands have not been studied yet. In this paper, we conduct channel measurements at 11, 16, 28, and 38 GHz frequency bands combined with large antenna arrays in an indoor office environment. The space-alternating generalized expectation-maximization (SAGE) algorithm is applied to obtain the multipath component (MPC) parameters. New propagation characteristics like spherical wavefront, cluster birth-death, and non-stationarity over antenna array axis are validated for the four mmWave bands by investigating the temporal-spatial channel characteristics like power delay profile (PDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS). The results indicate that massive MIMO effects should be fully considered for mmWave channel models under systems with large antenna arrays.

Performance comparison of six massive MIMO channel models

This paper compares the spatial cross-correlation functions (CCFs) and normalized channel capacities of four recently proposed massive multiple-input multiple-output (MIMO) channel models. Three of them are geometry-based stochastic models (GBSMs) and one is Kronecker-based stochastic model with birth death process on the array axis (KBSM-BD-AA). In addition, the impact of the elevation angles in the three dimensional (3-D) twin-cluster model and the 3-D unified GBSM on the resulting channel capacities, as well as the impact of polarization antennas in the 3-D unified GBSM on channel capacities, is investigated. Simulation results show that elevation angles can cause large impact on channel capacities and polarization antennas can halve the dimension of an antenna array at the cost of channel capacity loss.

Energy-spectral efficiency tradeoff of visible light communication systems

In this paper, we propose a new definition of the energy efficiency (EE) for indoor visible light communication (VLC) systems and further investigate the tradeoff between the EE and spectral efficiency (SE). Different from the conventional concept of the EE in wireless communication systems, the newly defined EE in VLC systems utilizes the optical communication power instead of radio frequency power to evaluate the consumed energy. Some key parameters (e.g., horizontal distance and vertical distance) in optical transmission circumstances are included when calculating the SE. The nonlinearity of light-emitting diode (LED) is also considered in the EE-SE tradeoff and such nonlinearity can be eliminated by pre-distortion. In particular, the relation between the electrical power and optical power can be derived based on the LED nonlinearity. Numerical results verify the presented EE-SE tradeoff. It is shown that the geometric parameters and the LED turn-on voltage (TOV) can significantly affect the relationship of the EE and SE.

Design principles for simultaneous wireless information and power transmission systems

By integrating conventional wireless power transfer (WPT) and wireless information transfer (WIT) into one efficient and creative transmission system, the simultaneous wireless information and power transfer (SWIPT) system has been considered as one promising paradigm for energy harvesting. To make the concept of SWIPT feasible and fully explore its underlying benefits, the practical system design is needed. In this paper, a potential SWIPT architecture with threshold-based switch (TBS) and signal mapping (SM) is proposed. The fundamental design principles, as well as several challenges are addressed based on the proposed architecture, which may provide the theoretical benchmark for the feasible system design in the future.

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.