Hybrid beamforming (HB) has been widely studied for reducing the number of costly radio frequency (RF) chains in massive multiple-input multiple-output (MIMO) systems. However, previous works on HB are limited to a single user equipment (UE) or a single group of UEs, employing the frequency-flat first-level analog beamforming (AB) that cannot be applied to multiple groups of UEs served in different frequency resources in an orthogonal frequency-division multiplexing (OFDM) system. In this paper, a novel HB algorithm with unified AB based on the spatial covariance matrix (SCM) knowledge of all UEs is proposed for a massive MIMO-OFDM system in order to support multiple groups of UEs. The proposed HB method with a much smaller number of RF chains can achieve more than 95% performance of full digital beamforming. In addition, a novel practical subspace construction (SC) algorithm based on partial channel state information is proposed to estimate the required SCM. The proposed SC method can offer more than 97% performance of the perfect SCM case. With the proposed methods, significant cost and power savings can be achieved without large loss in performance. Furthermore, the proposed methods can be applied to massive MIMO-OFDM systems in both time-division duplex and frequency-division duplex.

The demand for data service is increasing dramatically and wireless systems with high throughput and the capability to serve a large number of User Equipments (UEs) are desired. The massive MIMO system is considered as one of the most promising systems for the fifth generation of mobile telecommunication technology. Compared to conventional wireless systems, the spectrum efficiency of massive MIMO can be increased by an order of magnitude as tens of UEs can be served on the same time-frequency resource by exploring spatial multiplexing Though extensively studied, massive MIMO still faces tough practical issues, such as excessive cost for fronthaul, channel estimation, beamforming matrix calculation within the latency requirement, signal quality at the deep fading zones or cell edge, etc. This paper presents a repeater-enhanced massive MIMO system with improved channel quality and reduced fronthaul cost, where Amplify-and-Forward Repeaters (AFRs) are employed without additional baseband processing at the base station or UEs. The disadvantages of AFRs, such as extra delay, increased delay spread and compromised channel reciprocity, are discussed and the corresponding solutions are suggested. Performances of different beamforming methods are analyzed and verified by simulations.

Massive Multiple-Input Multiple-Output (MIMO) systems, dense Small-Cells (SCs), and full duplexing are three candidates for next-generation wireless systems. The cooperation of the three techniques could offer more benefits, e.g., SC in-band wireless backhaul in massive MIMO systems. In this paper, three strategies of SC in-band wireless backhaul in massive MIMO systems are introduced and compared. Simulation results show that SC in-band wireless backhaul has the potential to improve the throughput for massive MIMO systems, and applying full-duplexing techniques at SCs could provide greater gain.

Massive multiple-input multiple-output (MIMO) systems, dense small-cells (SCs), and full duplex are three candidate techniques for next-generation communication systems. The cooperation of next-generation techniques could offer more benefits, e.g., SC in-band wireless backhaul in massive MIMO systems. In this paper, three strategies of SC in-band wireless backhaul in massive MIMO systems are introduced and compared, i.e., complete time-division duplex (CTDD), zero-division duplex (ZDD), and ZDD with interference rejection (ZDD-IR). Simulation results demonstrate that SC in-band wireless backhaul has the potential to improve the throughput for massive MIMO systems. Specifically, among the three strategies, CTDD is the simplest one and could achieve decent throughput improvement. Depending on conditions, with the self-interference cancellation capability at SCs, ZDD could achieve better throughput than CTDD, even with residual self-interference. Moreover, ZDD-IR requires the additional interference rejection process at the BS compared to ZDD, but it could generally achieve better throughput than CTDD and ZDD.

The millimeter Wave (mmWave) is considered as a promising technology of the future fifth-generation wireless systems due to the currently underutilized multi-GHz spectrum surrounding the 60GHz carrier frequency, particularly with the recent advances in low cost sub-terahertz semiconductor circuitry. Since the mmWave suffers high attenuation during transmission, the repeater is a key technique to enable mmWave systems with seamless coverage. This paper considers the following practical design issues of Amplified-and-Forward Repeater (AFR) enhanced mmWave systems. First, the distribution of excess delay through multiple AFR hops is derived, which helps the design of cyclic-prefix length to avoid inter-symbol interference in orthogonal frequency division multiplexing systems. In addition, we show that the AFR decreases the channel coherence bandwidth and then propose a novel AFR design with a finite impulse response filter based channel equalizer to address this problem. Simulation results show that the proposed method is able to significantly increase channel coherence bandwidth and effectively reduce bit error rate.

Zero-Forcing (ZF) has been considered as one of the potential practical precoding and detection method for massive MIMO systems. One of the most important advantages of massive MIMO is the capability of supporting a large number of users in the same time-frequency resource, which requires much larger dimensions of matrix inversion for ZF than conventional multi-user MIMO systems. In this case, Neumann Series (NS) has been considered for the Matrix Inversion Approximation (MIA), because of its suitability for massive MIMO systems and its advantages in hardware implementation. The performance-complexity trade-off and the hardware implementation of NS-based MIA in massive MIMO systems have been discussed. In this paper, we analyze the effects of the ratio of the number of massive MIMO antennas to the number of users on the performance of NS-based MIA. In addition, we derive the approximation error estimation formulas for different practical numbers of terms of NS-based MIA. These results could offer useful guidelines for practical massive MIMO systems.

Large-scale MIMO systems have been considered as one of the possible candidates for the next-generation wireless communication technique, due to their potential to provide significant higher throughput than conventional wireless systems. For such systems, Zero-Forcing (ZF) and Conjugate Beamforming (CB) precoding have been considered as two possible practical spatial multiplexing techniques, and their average achievable sum rates have been derived on the sum power constraint. However, in practice, the transmitting power at a base station is constrained under each antenna. In this case, the optimal power allocation is a very difficult problem. In this paper, the suboptimal power allocation methods for both ZF-based and CB-based precoding in large-scale MIMO systems under per-antenna constraint are investigated, which could provide useful references for practice.

This paper provides a solution to a critical issue in large-scale Multi-User Multiple-Input Multiple-Output (MU-MIMO) communication systems: how to estimate the Signal-to-Interference-plus-Noise-Ratios (SINRs) and their expectations in MU-MIMO mode at the Base Station (BS) side when only the Channel Quality Information (CQI) in Single-User MIMO (SU-MIMO) mode and non-ideal Channel State Information (CSI) are known? A solution to this problem would be very beneficial for the BS to predict the capacity of MU-MIMO and choose the proper modulation and channel coding for MU-MIMO. To that end, this paper derives a normalized volume formula of a hyperball based on the probability density function of the canonical angle between any two points in a complex Grassmann manifold, and shows that this formula provides a solution to the aforementioned issue. It enables the capability of a BS to predict the capacity loss due to non-ideal CSI, group users in MU-MIMO mode, choose the proper modulation and channel coding, and adaptively switch between SU-MIMO and MU-MIMO modes, as well as between Conjugate Beamforming (CB) and Zero-Forcing (ZF) precoding. Numerical results are provided to verify the validity and accuracy of the solution.

For Multiple-Input Multiple-Output (MIMO) systems with frequency selective fading channels, Bit-Interleaved Coded Multiple Beamforming (BICMB) with Orthogonal Frequency Division Multiplexing (OFDM) can be employed to offer both spatial and multipath diversity, making it an important technique. Nevertheless, analyzing its diversity is a challenging problem. In this paper, the diversity analysis of BICMB-OFDM is carried out. First, the maximum achievable diversity is derived and a full diversity condition is proved. Then, the performance degradation due to the subcarrier correlation is investigated. Finally, the subcarrier grouping technique is applied to combat the performance degradation and provide multi-user compatibility.

Multiple-Input Multiple-Output (MIMO) wireless systems have drawn substantial interest because they can offer high spectral efficiency and performance in a given bandwidth. Beamforming techniques have been applied to increase throughput or performance for flat fading channels, when Channel State Information at the Transmitter (CSIT) is available. Although uncoded multiple beamforming cannot offer full diversity, Bit-Interleaved Coded Multiple Beamforming (BICMB) applying channel coding can achieve both full diversity and full multiplexing if the code rate Rc and the number of employed subchannels S satisfy the condition RcS≤1. Moreover, by constellation precoding, both uncoded and coded beamforming can achieve full diversity and full multiplexing simultaneously for any RcS value with increased decoding complexity. In this dissertation, Perfect Coded Multiple Beamforming (PCMB) and BICMB with Perfect Coding (BICMB-PC) are proposed. They apply Perfect Space-Time Block Codes (PSTBCs) and achieve both full diversity and full multiplexing for uncoded and coded beamforming respectively. PCMB has substantially lower decoding complexity than a MIMO system employing PSTBC. Its decoding complexity is also much lower than fully precoded multiple beamforming in dimensions 2 and 4. BICMB-PC has much lower decoding complexity than BICMB with full precoding in these two dimensions, and the complexity gain is greater than the uncoded case. MIMO techniques have been incorporated with Orthogonal Frequency Division Multiplexing (OFDM) for frequency selective fading channels. BICMB-OFDM can provide spatial diversity, multipath diversity, spatial multiplexing and frequency multiplexing simultaneously. In this dissertation, the diversity analysis of BICMB-OFDM is carried out, and a full diversity condition RcSL≤1 is proved, where S and L are the number of parallel streams at each subcarrier and the number of channel taps respectively. Furthermore, a full-diversity precoding design to overcome the restriction RcSL≤1 is developed with minimum decoding complexity. Although precoding techniques can increase the performance of beamforming systems, their Maximum Likelihood (ML) decoding has high complexity. Sphere Decoding (SD) is an alternative for ML decoding with reduced complexity. Additionally, the complexity of SD can be further reduced. In this dissertation, a new technique is introduced for both uncoded and coded MIMO systems, which substantially decreases the computational complexity of SD.

Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate, nonvanishing constant minimum determinant, uniform average transmitted energy per antenna, and good shaping. However, the high decoding complexity is a critical issue for practice. When the Channel State Information (CSI) is available at both the transmitter and the receiver, Singular Value Decomposition (SVD) is commonly applied for a Multiple-Input Multiple-Output (MIMO) system to enhance the throughput or the performance. In this paper, two novel techniques, Perfect Coded Multiple Beamforming (PCMB) and Bit-Interleaved Coded Multiple Beamforming with Perfect Coding (BICMB-PC), are proposed, employing both PSTBCs and SVD with and without channel coding, respectively. With CSI at the transmitter (CSIT), the decoding complexity of PCMB is substantially reduced compared to a MIMO system employing PSTBC, providing a new prospect of CSIT. Especially, because of the special property of the generation matrices, PCMB provides much lower decoding complexity than the state-of-the-art SVD-based uncoded technique in dimensions 2 and 4. Similarly, the decoding complexity of BICMB-PC is much lower than the state-of-the-art SVD-based coded technique in these two dimensions, and the complexity gain is greater than the uncoded case. Moreover, these aforementioned complexity reductions are achieved with only negligible or modest loss in performance.

In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can achieve performance equivalent to full search Maximum Likelihood (ML) decoding with reduced complexity. Several researchers reported techniques that reduce the complexity of SD further. In this paper, a new technique is introduced which decreases the computational complexity of SD substantially, without sacrificing performance. The reduction is accomplished by deconstructing the decoding metric to decrease the number of computations and exploiting the structure of a lattice representation. Simulation results show that this approach achieves substantial gains for the average number of real multiplications and real additions needed to decode one transmitted vector symbol. As an example, for a 4 × 4 MIMO system, the gains in the number of multiplications are 85% with 4-QAM and 90% with 64-QAM, at low SNR.

We present and analyze the performance of constellation precoded beamforming. This multi-input multi-output transmission technique is based on the singular value decomposition of a channel matrix. In this work, the beamformer is precoded to improve its diversity performance. It was shown previously that while single beamforming achieves full diversity without channel coding, multiple beamforming results in diversity loss. In this paper, we show that a properly designed constellation precoder makes uncoded multiple beamforming achieve full diversity order. We also show that partially precoded multiple beamforming gets better diversity order than multiple beamforming without constellation precoder if the subchannels to be precoded are properly chosen. We propose several criteria to design the constellation precoder. Simulation results match the analysis, and show that precoded multiple beamforming actually outperforms single beamforming without precoding at the same system data rate while achieving full diversity order.

Multi-Input Multi-Output (MIMO) wireless communication systems commonly employ beamforming techniques with Singular Value Decomposition (SVD). In such systems, if no channel encoding is employed, the full diversity order provided by the channel is achieved when a single symbol is transmitted over multiple channels; however, this property is lost whenever multiple symbols are simultaneously transmitted. The full diversity order can be restored when channel coding is added to such a system. For example, when Bit-Interleaved Coded Modulation (BICM) is combined with this technique, the full diversity order of NM in an M ×N MIMO channel, transmitting S parallel streams is possible; provided SRc ≤ 1 where RC is the BICM convolutional code rate. In this paper, we present multiple beamforming with constellation precoding which can achieve the full diversity order with both uncoded and BICM-coded SVD systems. An analytical proof of this property is provided. In addition, to reduce the computational complexity of Maximum Likelihood (ML) decoding, we introduce a Sphere Decoding (SD) technique. This technique achieves several orders of magnitude reduction in computational complexity not only with respect to conventional ML decoding, but also, with respect to conventional SD.

The Golden Code is a full-rate full-diversity space-time code, which achieves maximum coding gain for Multiple-Input Multiple-Output (MIMO) systems with two transmit and two receive antennas. Since four information symbols taken from an M-QAM constellation are selected to construct one Golden Code codeword, a maximum likelihood decoder using sphere decoding has the worst-case complexity of O(M 4 ), when the Channel State Information (CSI) is available at the receiver. Previously, this worst-case complexity was reduced to O(M 2.5 ) without performance degradation. When the CSI is known by the transmitter as well as the receiver, beamforming techniques that employ singular value decomposition are commonly used in MIMO systems. In the absence of channel coding, when a single symbol is transmitted, these systems achieve the full diversity order provided by the channel. Whereas this property is lost when multiple symbols are simultaneously transmitted. However, uncoded multiple beamforming can achieve the full diversity order by adding a properly designed constellation precoder. For 2 × 2 Fully Precoded Multiple Beamforming (FPMB), the general worst-case decoding complexity is O(M). In this paper, Golden Coded Multiple Beamforming (GCMB) is proposed, which transmits the Golden Code through 2 × 2 multiple beamforming. GCMB achieves the full diversity order and its performance is similar to general MIMO systems using the Golden Code and FPMB, whereas the worst-case decoding complexity of O(√(M)) is much lower. The extension of GCMB to larger dimensions is also discussed.