43 research outputs found
Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems
The users at cell edge of a massive multiple-input multiple-output (MIMO)
system suffer from severe pilot contamination, which leads to poor quality of
service (QoS). In order to enhance the QoS for these edge users, soft pilot
reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are
proposed. Specifically, the users are divided into two groups according to
their large-scale fading coefficients, referred to as the center users, who
only suffer from modest pilot contamination and the edge users, who suffer from
severe pilot contamination. Based on this distinction, the SPR scheme is
proposed for improving the QoS for the edge users, whereby a cell-center pilot
group is reused for all cell-center users in all cells, while a cell-edge pilot
group is applied for the edge users in the adjacent cells. By extending the
classical block diagonalization precoding to a multi-cell scenario, the MBD
precoding scheme projects the downlink transmit signal onto the null space of
the subspace spanned by the inter-cell channels of the edge users in adjacent
cells. Thus, the inter-cell interference contaminating the edge users' signals
in the adjacent cells can be efficiently mitigated and hence the QoS of these
edge users can be further enhanced. Our theoretical analysis and simulation
results demonstrate that both the uplink and downlink rates of the edge users
are significantly improved, albeit at the cost of the slightly decreased rate
of center users.Comment: 13 pages, 12 figures, accepted for publication in IEEE Transactions
on Vehicular Technology, 201
Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming
The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing.
Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between and . Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than dB.
On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art
Analysis and Design of Cell-Free Massive MIMO Systems under Spatially Correlated Fading Channels
Mención Internacional en el título de doctorWireless communications have become a key pillar in our modern society. It can be hard to
think of a service that somehow does not rely on them. Particularly, mobile networks are one of
the most necessary technologies in our daily life. This produces that the demand for data rates
is by no means stopping from increasing. The cellular architecture is facing a crucial challenge
under limited performance by interference and spectrum saturation. This involves cell-edge
users experiencing poor performance due to the close vicinity of base stations (BSs) using
the same carrier frequency. Based on a combination of the coordinated multi-point (CoMP)
technique and traditional massive multiple-input multiple-output (MIMO) systems, cell-free
(CF) massive MIMO networks have irrupted as a solution for avoiding inter-cell interference
issues and for providing uniform service in large coverage areas. This thesis focuses on the
analysis and design of CF massive MIMO networks assuming a spatially correlated fading
model. A general-purpose channel model is provided and the whole network functioning is
given in detail.
Despite the many characteristics a CF massive MIMO system shares with conventional colocated
massive MIMO its distributed nature brings along new issues that need to be carefully
accounted for. In particular, the so-called channel hardening effect that postulates that the variance
of the compound wireless channel experienced by a given user from a large number of
transmit antennas tends to vanish, effectively making the channel deterministic. This critical
assumption, which permeates most theoretical results of massive MIMO, has been well investigated
and validated in centralized architectures, however, it has received little attention in the
context of CF massive MIMO networks. Hardening in CF architectures is potentially compromised
by the different large-scale gains each access point (AP) impinges on the transmitted
signal to each user, a condition that is further stressed when not all APs transmit to all users as
proposed in the user-centric (UC) variations of CF massive MIMO. In this document, the presence
of channel hardening in this new architecture scheme is addressed using distributed and
cooperative precoders and combiners and different power control strategies. It is shown that
the line-of-sight (LOS) component, spatially correlated antennas, and clustering schemes have
an impact on how the channel hardens. In addition, we examine the existent gap between the
estimated achievable rate and the true network performance when channel hardening is compromised. Exact closed-form expressions for both a hardening metric and achievable downlink
(DL) and uplink (UL) rates are given as well.
We also look into the pilot contamination problem in the UL and DL with different degrees
of cooperation between the APs. The optimum minimum mean-squared error (MMSE) processing
can take advantage of large-scale fading coefficients for canceling the interference of
pilot-sharing users and thus achieves asymptotically unbounded capacity. However, it is computationally
demanding and can only be implemented in a fully centralized network. Here,
sub-optimal schemes are derived that provide unbounded capacity with much lower complexity
and using only local channel estimates but global channel statistics. This makes them suited for
both centralized and distributed networks. In this latter case, the best performance is achieved
with a generalized maximum ratio combiner that maximizes a capacity bound based on channel
statistics only.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Rui Dinis.- Secretario: María Julia Fernández-Getino García.- Vocal: Carmen Botella Mascarel
Resource allocation and feedback in wireless multiuser networks
This thesis focuses on the design of algorithms for resource allocation and feedback in wireless multiuser and heterogeneous networks. In particular, three key design challenges expected to have a major impact on future wireless networks are considered: cross-layer scheduling; structured quantization codebook design for MU-MIMO networks with limited feedback; and resource allocation to provide physical layer security. The first design challenge is cross-layer scheduling, where policies are proposed for two network architectures: user scheduling in single-cell multiuser networks aided by a relay; and base station (BS) scheduling in CoMP. These scheduling policies are then analyzed to guarantee satisfaction of three performance metrics: SEP; packet delay; and packet loss probability (PLP) due to buffer overflow. The concept of the τ-achievable PLP region is also introduced to explicitly describe the tradeoff in PLP between different users. The second design challenge is structured quantization codebook design in wireless networks with limited feedback, for both MU-MIMO and CoMP. In the MU-MIMO network, two codebook constructions are proposed, which are based on structured transformations of a base codebook. In the CoMP network, a low-complexity construction is proposed to solve the problem of variable codebook dimensions due to changes in the number of coordinated BSs. The proposed construction is shown to have comparable performance with the standard approach based on a random search, while only requiring linear instead of exponential complexity. The final design challenge is resource allocation for physical layer security in MU-MIMO. To guarantee physical layer security, the achievable secrecy sum-rate is explicitly derived for the regularized channel inversion (RCI) precoder. To improve performance, power allocation and precoder design are jointly optimized using a new algorithm based on convex optimization techniques
Resource allocation and feedback in wireless multiuser networks
This thesis focuses on the design of algorithms for resource allocation and feedback in wireless multiuser and heterogeneous networks. In particular, three key design challenges expected to have a major impact on future wireless networks are considered: cross-layer scheduling; structured quantization codebook design for MU-MIMO networks with limited feedback; and resource allocation to provide physical layer security. The first design challenge is cross-layer scheduling, where policies are proposed for two network architectures: user scheduling in single-cell multiuser networks aided by a relay; and base station (BS) scheduling in CoMP. These scheduling policies are then analyzed to guarantee satisfaction of three performance metrics: SEP; packet delay; and packet loss probability (PLP) due to buffer overflow. The concept of the τ-achievable PLP region is also introduced to explicitly describe the tradeoff in PLP between different users. The second design challenge is structured quantization codebook design in wireless networks with limited feedback, for both MU-MIMO and CoMP. In the MU-MIMO network, two codebook constructions are proposed, which are based on structured transformations of a base codebook. In the CoMP network, a low-complexity construction is proposed to solve the problem of variable codebook dimensions due to changes in the number of coordinated BSs. The proposed construction is shown to have comparable performance with the standard approach based on a random search, while only requiring linear instead of exponential complexity. The final design challenge is resource allocation for physical layer security in MU-MIMO. To guarantee physical layer security, the achievable secrecy sum-rate is explicitly derived for the regularized channel inversion (RCI) precoder. To improve performance, power allocation and precoder design are jointly optimized using a new algorithm based on convex optimization techniques