615 research outputs found
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment
The application of multiple-input multiple-output (MIMO) techniques to
non-orthogonal multiple access (NOMA) systems is important to enhance the
performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for
downlink and uplink transmission is proposed by applying the concept of signal
alignment. By using stochastic geometry, closed-form analytical results are
developed to facilitate the performance evaluation of the proposed framework
for randomly deployed users and interferers. The impact of different power
allocation strategies, such as fixed power allocation and cognitive radio
inspired power allocation, on the performance of MIMO-NOMA is also
investigated. Computer simulation results are provided to demonstrate the
performance of the proposed framework and the accuracy of the developed
analytical results
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.Comment: Accepted at ICC 201
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
The grand objective of 5G wireless technology is to support three generic
services with vastly heterogeneous requirements: enhanced mobile broadband
(eMBB), massive machine-type communications (mMTC), and ultra-reliable
low-latency communications (URLLC). Service heterogeneity can be accommodated
by network slicing, through which each service is allocated resources to
provide performance guarantees and isolation from the other services. Slicing
of the Radio Access Network (RAN) is typically done by means of orthogonal
resource allocation among the services. This work studies the potential
advantages of allowing for non-orthogonal sharing of RAN resources in uplink
communications from a set of eMBB, mMTC and URLLC devices to a common base
station. The approach is referred to as Heterogeneous Non-Orthogonal Multiple
Access (H-NOMA), in contrast to the conventional NOMA techniques that involve
users with homogeneous requirements and hence can be investigated through a
standard multiple access channel. The study devises a communication-theoretic
model that accounts for the heterogeneous requirements and characteristics of
the three services. The concept of reliability diversity is introduced as a
design principle that leverages the different reliability requirements across
the services in order to ensure performance guarantees with non-orthogonal RAN
slicing. This study reveals that H-NOMA can lead, in some regimes, to
significant gains in terms of performance trade-offs among the three generic
services as compared to orthogonal slicing.Comment: Submitted to IEE
Capacity-Achieving Iterative LMMSE Detection for MIMO-NOMA Systems
This paper considers a iterative Linear Minimum Mean Square Error (LMMSE)
detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO)
systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE
detection greatly reduces the system computational complexity by departing the
overall processing into many low-complexity distributed calculations. However,
it is generally considered to be sub-optimal and achieves relatively poor
performance. In this paper, we firstly present the matching conditions and area
theorems for the iterative detection of the MIMO-NOMA systems. Based on the
proposed matching conditions and area theorems, the achievable rate region of
the iterative LMMSE detection is analysed. We prove that by properly design the
iterative LMMSE detection, it can achieve (i) the optimal sum capacity of
MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of
MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.Comment: 6pages, 5 figures, accepted by IEEE ICC 2016, 23-27 May 2016, Kuala
Lumpur, Malaysi
On the Performance Gain of NOMA over OMA in Uplink Communication Systems
In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of
non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in
uplink cellular communication systems. A base station equipped with a
single-antenna, with multiple antennas, and with massive antenna arrays is
considered both in single-cell and multi-cell deployments. In particular, in
single-antenna systems, we identify two types of gains brought about by NOMA:
1) a large-scale near-far gain arising from the distance discrepancy between
the base station and users; 2) a small-scale fading gain originating from the
multipath channel fading. Furthermore, we reveal that the large-scale near-far
gain increases with the normalized cell size, while the small-scale fading gain
is a constant, given by = 0.57721 nat/s/Hz, in Rayleigh fading
channels. When extending single-antenna NOMA to -antenna NOMA, we prove that
both the large-scale near-far gain and small-scale fading gain achieved by
single-antenna NOMA can be increased by a factor of for a large number of
users. Moreover, given a massive antenna array at the base station and
considering a fixed ratio between the number of antennas, , and the number
of users, , the ESG of NOMA over OMA increases linearly with both and
. We then further extend the analysis to a multi-cell scenario. Compared to
the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more
severe inter-cell interference due to the non-orthogonal transmissions.
Besides, we unveil that a large cell size is always beneficial to the ergodic
sum-rate performance of NOMA in both single-cell and multi-cell systems.
Numerical results verify the accuracy of the analytical results derived and
confirm the insights revealed about the ESG of NOMA over OMA in different
scenarios.Comment: 51 pages, 7 figures, invited paper, submitted to IEEE Transactions on
Communication
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
- …