Publication: Energy-efficient digital and hybrid precoding algorithms for interference-limited cellular networks
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2024-01-23
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2024-01-23
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Abstract
The fifth-generation (5G) of cellular communications demands high values of data rates, energy
efficiency (EE), reliability, and multi-connectivity. In this context, ultra-dense networks
(UDNs) are envisioned as a promising solution to meet the requirements of 5G and beyond
5G (B5G) communications. UDNs are characterized by the deployment of a large number
of small-cells (SCs) in the coverage area of the traditional macrocell. Each SC is composed
of a low-power small base station (SBS) that provides short-range communication to the user
equipment (UE). Network densification increases the spectral efficiency (SE), EE, spectrum
allocation, and coverage. However, their performance is limited by the increase of inter-user
interference caused by near SBSs that share the same time and frequency resources. Several
emerging technologies have been proposed to enable the development of UDNs. The use of
multiple-input multiple-output (MIMO) systems combined with precoding techniques has been
proposed to increase the SE and EE. Interference alignment (IA) arises as a precoding strategy
to manage interference while increasing the achievable degrees of freedom (DoF). However,
the IA algorithms available in the literature have several limitations. In this thesis, novel digital
and hybrid IA algorithms have been proposed to solve the main open problems of the existing
solutions and hence to further improve the performance of UDNs.
First, a two-tier network deployment is studied. Based on the cognitive radio (CR) paradigm,
it is assumed that the SBS applies spectrum sensing techniques to detect spectrum holes in
the frequency bands of the macrocell. The unoccupied bands are used by the SBS to transmit
information to its corresponding UE. The more challenging aspect of the proposed model is
the assumption that the SBS is equipped with an energy harvesting (EH) device that collects
energy from the environment. Unlike previous strategies that are based on a constant sensing
power value, novel power allocation policies are proposed in this thesis to maximize detection
performance under energy constraints. Assuming that energy consumption only depends on
the test statistic to detect the presence of signals, a closed-form expression is derived to relate
energy consumption with the number of processed samples and the technology-dependent
parameters of the processing unit. Since the obtained closed-form equation is concave with
respect to the processing power, dynamic power allocation policies are proposed to maximize
the probability of detection in offline and online scenarios based on convex optimization theory, dynamic programming (DP), and heuristic solutions with lower complexity (Constant Power
and Greedy policies). The performance of proposed policies is validated by simulations for
common detection techniques such as matched filter (MF), quadrature matched filter (QMF),
and energy detector (ED). The proposed CR-EH policies enable the continuous operation of
energy self-sufficient communications nodes guaranteeing also a high level of SE.
Next chapters are addressed to the interference cancellation on a dense deployment of SCs.
Clustered-IA has been widely studied to manage the interference in these dense scenarios.
Nonetheless, the setups considered in previous works relied on oversimplified channel models
and/or enforced single-stream transmission. In this thesis, a novel cluster design based on sizerestricted
𝑘-means algorithm is proposed to divide the SCs into different clusters considering
both path loss and shadowing effects. Therefore, a more realistic solution than those available
in the current literature is provided. Unlike previous works, this clustering method can also
cater to spatial multiplexing scenarios. Also, several design parameters such as the number
of transmit antennas, multiplexed data streams, and deployed SBSs are analyzed in order to
identify trade-offs between performance and complexity. The relationship between density of
network elements per area unit and performance is also investigated. Moreover, it is shown that
the SE degradation due to the inter-cluster interference in UDNs points to the need of designing
an interference management algorithm that accounts for both, intra-cluster and inter-cluster
interference.
Reported IA techniques neglect the nonlinear distortion (NLD) induced by power amplifiers
(PAs). Thus, their performance is severely degraded in highly power-efficient transmissions operating
close to the saturation point. Distortion-aware precoding techniques have been studied
to reduce NLD in single base station scenarios either single-user or multi-user. Nevertheless, its
extension to UDNs with multiple SBSs and UEs is not straightforward. In this thesis, a novel
IA algorithm, named NLD-IA, is proposed to alternatively design the precoding and combining
vectors to cancel both interference and nonlinearities, so that signal-to-interference-plus-noise
ratio (SINR) is maximized in UDNs. Precoding vectors are obtained via a non-convex optimization
problem that models the NLD correlation. An analytical solution based on the Newton
method over the complex field is developed to solve this problem with low computational
complexity. Simulation results show that the proposed NLD-IA significantly reduces interference
and NLD. Thus, it significantly outperforms previous IA algorithms for a wide range of
signal-to-noise ratio (SNR), network densification, and saturation level. Another problem of network densification is that spectral resources are being saturated. An
attractive solution is to extend the communication services to the millimeter-wave (mmWave)
frequencies that provide a huge available spectrum to support broadband applications. Nevertheless,
the implementation of fully digital (FD) precoding strategies becomes prohibitive
in massive MIMO (mMIMO) systems working at mmWave due to their hardware complexity.
Therefore, hybrid digital-analog beamforming (HB) is considered to reduce the number
of radio frequency (RF) chains. When the analog stage is implemented with low-resolution
phase shifters (PSs), the SE is severely affected. In this thesis, a novel HB method to achieve
a near-optimal FD performance for mMIMO systems with the minimum required number of
RF chains is developed. The reduction of RF chains arises as a key procedure to obtain an EE
design. The proposed HB scheme is based on a quantization-aware matrix composition (QMC)
to systematically compensate for the residual quantization errors. Furthermore, a quantization
strategy, called sum Euclidean quantization (SEQ), is introduced to improve the accuracy of
the analog stage. Numerical results reveal that, unlike previously reported HB methods, a nearoptimal
SE can be achieved with our proposal while minimizing the number of RF chains and
thus, the hardware cost and power consumption.
On the other hand, although IA algorithms have been widely studied following an FD approach,
there are only a few works on HB-IA. Therefore, a new HB-IA is also proposed in this thesis for
UDNs based on an iterative partial construction (IPC) procedure with the novelty of awareness
of PSs quantization. The IPC algorithm relies on algebraic properties to iteratively compute the
analog and digital matrices, via a partial submatrix composition, such that the Euclidean distance
to the FD-IA solution is reduced. An additional digital block, based on an IA algorithm,
is also applied to cancel the residual interference. Numerical results reveal that the proposed
HB IPC-IA significantly outperforms reported works achieving a near FD-IA performance.
Since MIMO precoding is a key enabling technology for 5G/B5G cellular networks, the 3rd
Generation Partnership Project (3GPP) has standardized precoding techniques for single-user
and multi-user MIMO systems to achieve a balanced trade-off between performance, complexity,
and signal overhead. The implementation of these precoding strategies is covered in this
thesis providing a comprehensive guide. The performance of the 5G New Radio (NR) precoder
is compared with theoretical precoding techniques such as singular value decomposition
(SVD) and block-diagonalization (BD) to quantify the margin of improvement of the standardized
methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors
on the system performance is analyzed. For a realistic framework, the SE values are obtained
for a practical deployment based on a clustered delay line (CDL) channel model. These results
provide valuable insights for system designers about the implementation and performance of
the 5G-NR precoding matrices.
Finally, a practical implementation of IA algorithms on a hardware platform using universal
software radio peripherals (USRPs) is addressed. A synchronization stage, based on the
Schmidl and Cox method, is implemented. In contrast to previous works, a hardware testbed,
able to model heterogeneous SINR networks, is proposed. The role of both closed and open
loop for channel estimation is evaluated. Then, the impact of channel state information (CSI)
updating on the SE and bit error rate (BER) is also analyzed. All the results are based on real
measurements providing a clear understanding of the benefits and limitations of IA techniques
in interference-limited cellular networks.
Description
Mención Internacional en el título de doctor
Keywords
5G, Cellular communications, Multiple-input multiple-output, MIMO, Hybrid beamforming, Digital precoding, Energy harvesting