Full-rank Channel Research Articles

Over-the-Air Testing of a Massive MIMO Antenna with a Full-Rank Channel Matrix.

This paper presents an over-the-air testing method in which a full-rank channel matrix is created for a massive multiple-input multiple-output (MIMO) antenna system utilizing a fading emulator with a small number of scatterers. In the proposed method, in order to mimic a fading emulator with a large number of scatterers, the scatterers are virtually positioned by rotating the massive MIMO antenna. The performance of a 64-element quasi-half-wavelength dipole circular array antenna was evaluated using a two-dimensional fading emulator. The experimental results reveal that a large number of available eigenvalues are obtained from the channel response matrix, confirming that the proposed method, which utilizes a full-rank channel matrix, can be used to assess a massive MIMO antenna system.

Automatic modulation classification over MIMO amplify and forward (AF)-relay fading channels

Automatic Modulation Classification (AMC) plays an important role in many civilian applications such as spectrum monitoring (SM) and cognitive radio (CR). The majority of the AMC algorithms for MIMO systems are developed for rich scattering environments, which offer a full rank channel matrix. A rank deficient channel such as a keyhole channel severely affects the AMC performance. It is reported that cooperative relaying is an attractive solution to overcome the rank deficiency of multiple-input multiple-output (MIMO) channels. This work investigates the effect of cooperative relaying on the AMC performance. A higher-order cumulant (HOC)-based AMC method, namely the relay assisted RA-AMC method, is proposed to classify a pool of modulation types, namely BPSK, OQPSK, QPSK, π4-QPSK, 8-PSK and 16-QAM over an amplify and forward (AF) relay network. Simulation results show that the proposed method performs reasonably well over AF-relay fading channels.

Channel capacity investigation of a linear massive MIMO system using spherical wave model in LOS scenarios

Massive multiple-input multiple-output (MIMO) is a key technology for the 5th generation (5G) of wireless communication systems. The traditional plane wave channel model (PWM) is often not suitable for the large antenna structure, and in certain cases should be replaced by the more accurate spherical wave model (SWM). By using the spherical wave characterization method, this paper investigates the channel capacity performance of a linear massive MIMO system in line-of-sight (LOS) scenarios. Two types of access settings, the point to point (PTP) system and multi-user (MU) system, are considered. In the PTP setting, a geometrical optimization is performed to obtain configurations that are able to generate a full rank channel matrix for a linear massive MIMO system, which yields full spatial diversity even in LOS scenarios. Compared with the approximate and commonly applied rank-1 PWM, this is very useful for fixed wireless access and radio relay systems requiring high throughput. For the MU case, we compare the eigenvalue distributions of the LOS channels using the plane wave and spherical wave characterization method, and sum rate results are obtained by Monte Carlo simulations. The results show that MU systems using the more realistic and accurate SWM can achieve a higher sum rate than results from the PWM. This is beneficial and informative when designing massive MIMO wireless networks.

Survey of Large-Scale MIMO Systems

The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.

Adaptive low-rank channel estimation for multi-band OFDM ultra-wideband communications

In this paper, an adaptive channel estimation scheme based on the reduced-rank (RR) Wiener filtering (WF) technique is proposed for multi-band (MB) orthogonal frequency division multiplexing (OFDM) ultra-wideband (UWB) communication systems in multipath fading channels. This RR-WF-based algorithm employs an adaptive fuzzy-inference-controlled (FIC) filter rank. Additionally, a comparative investigation into various channel estimation schemes is presented as well for MB-OFDM UWB communication systems. As a consequence, the FIC RR-WF channel estimation algorithm is capable of producing the bit-error-rate (BER) performance similar to that of the full-rank WF channel estimator and superior than those of other interpolation-based channel estimation schemes.

Maximum-likelihood noncoherent OSTBC detection with polynomial complexity

We prove that maximum-likelihood (ML) noncoherent sequence detection of orthogonal space-time block coded signals can always be performed in polynomial time with respect to the sequence length for static Rayleigh, correlated (in general) channels. Moreover, using recent results on efficient maximization of rank-deficient quadratic forms over finite alphabets, we develop an algorithm that performs ML noncoherent sequence detection with polynomial complexity. The order of the polynomial complexity of the proposed receiver equals two times the rank of the covariance matrix of the vectorized channel matrix. Therefore, the lower the Rayleigh channel covariance rank the lower the receiver complexity. Instead, for Ricean channel distribution, we prove that polynomial complexity is attained through the proposed receiver as long as the mean channel vector is in the range of the covariance matrix of the vectorized channel matrix. Hence, full-rank channel correlation is desired to guarantee polynomial ML noncoherent detection complexity for the case of static Ricean fading. Our results are presented for the general case of block-fading Rayleigh or Ricean channels where we provide conditions under which ML noncoherent sequence detection can be performed in polynomial time through our algorithm.

A probabilistic model for sub-carrier allocation in OFDMA systems

Orthogonal frequency division multiple-access technique showed a successful utilization of channel features. It implements an orthogonal sub-carrier space to be shared among different users. The management of these sub-carriers in both power and frequency allocation is reflected on the systems performance as better utilization of bandwidth, and hence, better capacity is obtained. Sub-carrier allocation is used to avoid deep fading that might occur at some user’s locations but not at other user’s locations. In this paper, we devise an algorithm based on probabilistic model for sub-carrier allocation to avoid deep fading in some user’s signals. By controlling the sub-carrier allocation for each user, we can create a full rank channel for each user and hence, provide maximum capacity for the system. Simulation results showed that using the devised algorithm will avoid deep fading and utilize the bandwidth up to 40% more than localized allocation strategies.

Hierarchical Space-Time Block Code Recognition Using Correlation Matrices

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. The results of numerous investigations about symbol timing estimation, modulation recognition as well as identification of the number of transmitters have been reported in the literature. But, to our knowledge, none of them have dealt with the recognition of the Space-Time Block Codes (STBC) used in multiple transmitter communications. In order to blindly recognize the STBC of a wireless communication, this paper proposes a method based on the space-time correlations of the received signals. Under perfect timing synchronization and under ideal conditions (full rank channel and a number of receivers greater or equal to the number of transmitters), it shows that the Frobenius norms of these statistics present non-null values whose positions only depend on the STBC at the transmitter side. A classifier for the space-time code recognition of 5 linear STBC (Spatial Multiplexing, Alamouti Coding, and 3 Orthogonal STBC using 3 antennas) is presented. Simulations show that the proposed method performs well even at low signal-to-noise ratios.

Degrees of Freedom for the MIMO Interference Channel

In this correspondence, we show that the exact number of spatial degrees of freedom (DOF) for a two user nondegenerate (full rank channel matrices) multiple-input-multiple-output (MIMO) Gaussian interference channel with M1, M2 antennas at transmitters 1, 2 and N1, N2 antennas at the corresponding receivers, and perfect channel knowledge at all transmitters and receivers, is min{M1 + M2, N1 + M2, max(M1, N2), max(M2, N1)}. A constructive achievability proof shows that zero forcing is sufficient to achieve all the available DOF on the two user MIMO interference channel. We also show through an example of a share-and-transmit scheme how the gains of transmitter cooperation may be entirely offset by the cost of enabling that cooperation so that the available DOF are not increased.

Achievable Performance of Orthogonal STBC Over Spatially Correlated Rician Channels

The effect of spatial correlation on the performance of orthogonal space-time block codes (OSTBCs) over multiple-input-multiple-output (MIMO) Rician fading channels is studied. Asymptotic error-rate formulas for OSTBC with high average signal-to-noise ratios (ASNRs) over arbitrarily correlated Rician MIMO channels are derived in terms of the diversity and coding gains. Our results show that, in correlated fading, the phase vector phi of the channel line-of-sight (LOS) components affects the effective Rice K-factor at the OSTBC receiver output and, hence, may result in a coding gain that is significantly higher than that for independent Rician MIMO channels. Furthermore, when the channel covariance matrix is rank deficient and under some additional mild conditions, the error and outage probabilities of OSTBC achieve those in a nonfading additive-white-Gaussian-noise channel. For both cases of full-rank and rank-deficient channel covariance matrices, analytical expressions of optimal and worst case phase vectors phi, and exact upper and lower bounds of OSTBC performance are derived. These results provide new insights into the achievable performance of OSTBC over correlated Rician MIMO channels and, if incorporated into future multiple antenna systems design, will bring about significant performance enhancement

Blind MIMO channel identification from second order statistics using rank deficient channel convolution matrix

For multiuser systems, several direct blind identification algorithms require that the linear multiple-input multiple-output (MIMO) system have a full rank convolution matrix. This condition requires that the system transfer function be irreducible and column reduced. We show that this restrictive identification condition can be relaxed for some direct blind identification methods to accommodate more practical scenarios. Algorithms such as the outer-product decomposition algorithm only require minor length adjustment to its processing window without the column-reduced condition. This result allows direct blind identification methods to be applicable to MIMO without requiring a full-rank channel convolution matrix.