2,261 research outputs found
Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes
— A promising massive multiple input multiple output (M-MIMO) system is required to meet the growing need for highly traffic data, highly-resolution of streaming video, and intelligent communication on the fifth-generation wireless networks (5G). M-MIMO systems are essential for the optimization of the trade between energy efficiency (EE), throughput (R), and spectral _efficiency (SE) in wireless 5G networks. M-MIMO system architecture is proposed in this paper to enhance the trade-off between energy efficiency and uplink and downlink throughput at the optimum EE. Furthermore, using linear precoding techniques such as M MMSE, RZF, ZF, and MR, the EE-SE trade-off is optimized for uplink and downlink (M-MIMO) systems. The analysis of simulation results proved that throughput (R) is enhanced by increasing the number of antennas at optimum EE. After that, the proposed trading scheme is optimized and improved using M_MMSE compared to RZF, ZF. Finally, the results prove that M_MMSE gives the optimum trade-off between EE and R at the proved optimum ratio between the number of active antennas and the number of active users UE
Performance Evaluation of Hybrid Precoder Design for Multi-User Massive MIMO Systems with Low-Resolution ADCs/DACs
This paper presents a comprehensive analysis and design of a hybrid precoding system tailored for mmWave multi-user massive MIMO systems in both downlink and uplink scenarios. The proposed system employs a two-stage precoding approach, incorporating UQ and NUQ techniques, along with low-resolution DACs in downlink and ADCs in uplink to address hardware limitations. The system considers Zero Forcing and Minimum Mean Square Error algorithms as digital precoding methods for the uplink scenario, while exploring the impact of different DAC resolutions on system performance. Extensive simulations reveal that the proposed system surpasses conventional analog beamforming methods, particularly in multi-user scenarios involving inter-user interference. In downlink, the system demonstrates a trade-off between SE and EE, achieving higher Energy Efficiency with NUQ. In uplink, NUQ and UQ converters exhibit similar performance trends regardless of the chosen combiner algorithm. The proposed system attains enhanced Spectral and Energy Efficiency while maintaining reduced complexity and overhead. The study significantly contributes to the advancement of efficient and effective mmWave multi-user massive MIMO systems by providing a thorough analysis of various quantization schemes and precoding techniques. The findings of this research are expected to aid in the optimization of 5G and beyond technologies, particularly in high-density deployment scenarios
Performance analysis of RIS-assisted cell-free massive MIMO systems with transceiver hardware impairments
Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated
Spectral-energy efficiency trade-off for next-generation wireless communication systems
The data traffic in cellular networks has had and will experience a rapid exponential
rise. Therefore, it is essential to innovate a new cellular architecture with
advanced wireless technologies that can offer more capacity and enhanced spectral
efficiency to manage the exponential data traffic growth. Managing such mass
data traffic, however, brings up another challenge of increasing energy consumption.
This is because it contributes into a growing fraction of the carbon dioxide
(CO2) emission which is a global concern today due to its negative impact on
the environment. This has resulted in creating a new paradigm shift towards both
spectral and energy efficient orientated design for the next-generation wireless access
networks. Acquiring both improved energy efficiency and spectral efficiency
has, nonetheless, shown to be a difficult goal to achieve as it seems improving one
is at the detriment to the other. Therefore, the trade-off between the spectral and
energy efficiency is of paramount importance to assess the energy consumption in
a wireless communication system required to attain a specific spectral efficiency.
This thesis looks into this problem. It studies the spectral-energy efficiency tradeoff
for some of the emerging wireless communication technologies which are seen
as potential candidates for the fifth generation (5G) mobile cellular system. The
focus is on the orthogonal frequency division multiple access (OFDMA), mobile
femtocell (MFemtocell), cognitive radio (CR), and the spatial modulation (SM).
Firstly, the energy-efficient resource allocation scheme for multi-user OFDMA
(MU-OFDMA) system is studied. The spectral-energy efficiency trade-off is
analysed under the constraint of maintaining the fairness among users. The
energy-efficient optimisation problem has been formulated as integer fractional
programming. We then apply an iterative method to simplify the problem to an
integer linear programming (ILP) problem.
Secondly, the spectral and energy efficiency for a cellular system with MFemtocell
deployment is investigated using different resource partitioning schemes.
Femtocells are low range, low power base stations (BSs) that improve the coverage
inside a home or office building. MFemtocell adopts the femtocell solution to be deployed in public transport and emergency vehicles. Closed-form expressions
for the relationships between the spectral and energy efficiency are derived for
a single-user (SU) MFemtocell network. We also study the spectral efficiency
for MU-MFemtocells with two opportunistic scheduling schemes.
Thirdly, the spectral-energy efficiency trade-off for CR networks is analysed at
both SU and MU CR systems against varying signal-to-noise ratio (SNR) values.
CR is an innovative radio device that aims to utilise the spectrum more efficiently
by opportunistically exploiting underutilised licensed spectrum. For the SU system,
we study the required energy to achieve a specific spectral efficiency for a
CR channel under two different types of power constraints in different fading environments.
In this scenario, interference constraint at the primary receiver (PR)
is also considered to protect the PR from harmful interference. At the system
level, we study the spectral and energy efficiency for a CR network that shares
the spectrum with an indoor network. Adopting the extreme-value theory, we
are able to derive the average spectral efficiency of the CR network.
Finally, we propose two innovative schemes to enhance the capability of (SM). SM
is a recently developed technique that is employed for a low complexity multipleinput
multiple-output (MIMO) transmission. The first scheme can be applied for
SU MIMO (SU-MIMO) to offer more degrees of freedom than SM. Whereas the
second scheme introduces a transmission structure by which the SM is adopted
into a downlink MU-MIMO system. Unlike SM, both proposed schemes do not
involve any restriction into the number of transmit antennas when transmitting
signals. The spectral-energy efficiency trade-off for the MU-SM in the massive
MIMO system is studied. In this context, we develop an iterative energy-efficient
water-filling algorithm to optimises the transmit power and achieve the maximum
energy efficiency for a given spectral efficiency.
In summary, the research presented in this thesis reveals mathematical tools to
analysis the spectral and energy efficiency for wireless communications technologies.
It also offers insight to solve optimisation problems that belong to a class
of problems with objectives of enhancing the energy efficiency
Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems
Massive MIMO systems promise high data rates by employing large number of
antennas, which also increases the power usage of the system as a consequence.
This creates an optimization problem which specifies how many antennas the
system should employ in order to operate with maximal energy efficiency. Our
main goal is to consider a base station with a fixed number of antennas, such
that the system can operate with a smaller subset of antennas according to the
number of active user terminals, which may vary over time. Thus, in this paper
we propose an antenna selection algorithm which selects the best antennas
according to the better channel conditions with respect to the users, aiming at
improving the overall energy efficiency. Then, due to the complexity of the
mathematical formulation, a tight approximation for the consumed power is
presented, using the Wishart theorem, and it is used to find a deterministic
formulation for the energy efficiency. Simulation results show that the
approximation is quite tight and that there is significant improvement in terms
of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications
Technologies, 12 pages, 8 figures, 2 table
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
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