586 research outputs found
Harvest the potential of massive MIMO with multi-layer techniques
Massive MIMO is envisioned as a promising technology for 5G wireless networks
due to its high potential to improve both spectral and energy efficiency.
Although the massive MIMO system is based on innovations in the physical layer,
the upper layer techniques also play important roles in harvesting the
performance gains of massive MIMO. In this article, we begin with an analysis
of the benefits and challenges of massive MIMO systems. We then investigate the
multi-layer techniques for incorporating massive MIMO in several important
network deployment scenarios. We conclude this article with a discussion of
open and potential problems for future research.Comment: IEEE Networ
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends
Non-orthogonal multiple access (NOMA) is an essential enabling technology for
the fifth generation (5G) wireless networks to meet the heterogeneous demands
on low latency, high reliability, massive connectivity, improved fairness, and
high throughput. The key idea behind NOMA is to serve multiple users in the
same resource block, such as a time slot, subcarrier, or spreading code. The
NOMA principle is a general framework, and several recently proposed 5G
multiple access schemes can be viewed as special cases. This survey provides an
overview of the latest NOMA research and innovations as well as their
applications. Thereby, the papers published in this special issue are put into
the content of the existing literature. Future research challenges regarding
NOMA in 5G and beyond are also discussed.Comment: to appear in IEEE JSAC, 201
Signal Processing for MIMO-NOMA: Present and Future Challenges
Non-orthogonal multiple access (NOMA), as the newest member of the multiple
access family, is envisioned to be an essential component of 5G mobile
networks. The combination of NOMA and multi-antenna multi-input multi-output
(MIMO) technologies exhibits a significant potential in improving spectral
efficiency and providing better wireless services to more users. In this
article, we introduce the basic concepts of MIMO-NOMA and summarize the key
technical problems in MIMO-NOMA systems. Then, we explore the problem
formulation, beamforming, user clustering, and power allocation of
single/multi-cluster MIMO-NOMA in the literature along with their limitations.
Furthermore, we point out an important issue of the stability of successive
interference cancellation (SIC) that arises using achievable rates as
performance metrics in practical NOMA/MIMO-NOMA systems. Finally, we discuss
incorporating NOMA with massive/millimeter wave MIMO, and identify the main
challenges and possible future research directions in this area.Comment: 14 pages (single column), 4 figures. This work has been accepted by
the IEEE Wireless Communications, the special issue of non-orthogonal
multiple access for 5
Multi-Beam NOMA for Hybrid mmWave Systems
In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA)
scheme for hybrid millimeter wave (mmWave) systems and study its resource
allocation. A beam splitting technique is designed to generate multiple analog
beams to serve multiple users for NOMA transmission. Compared to conventional
mmWave orthogonal multiple access (mmWave-OMA) schemes, the proposed scheme can
serve more than one user on each radio frequency (RF) chain. Besides, in
contrast to the recently proposed single-beam mmWave-NOMA scheme which can only
serve multiple NOMA users within the same beam, the proposed scheme can perform
NOMA transmission for the users with an arbitrary angle-of-departure (AOD)
distribution. This provides a higher flexibility for applying NOMA in mmWave
communications and thus can efficiently exploit the potential multi-user
diversity. Then, we design a suboptimal two-stage resource allocation for
maximizing the system sum-rate. In the first stage, assuming that only analog
beamforming is available, a user grouping and antenna allocation algorithm is
proposed to maximize the conditional system sum-rate based on the coalition
formation game theory. In the second stage, with the zero-forcing (ZF) digital
precoder, a suboptimal solution is devised to solve a non-convex power
allocation optimization problem for the maximization of the system sum-rate
which takes into account the quality of service (QoS) constraint. Simulation
results show that our designed resource allocation can achieve a
close-to-optimal performance in each stage. In addition, we demonstrate that
the proposed multi-beam mmWave-NOMA scheme offers a higher spectral efficiency
than that of the single-beam mmWave-NOMA and the mmWave-OMA schemes.Comment: Submitted for possible journal publicatio
A Spatial Basis Coverage Approach For Uplink Training And Scheduling In Massive MIMO Systems
Massive multiple-input multiple-output (massive MIMO) can provide large
spectral and energy efficiency gains. Nevertheless, its potential is
conditioned on acquiring accurate channel state information (CSI). In time
division duplexing (TDD) systems, CSI is obtained through uplink training which
is hindered by pilot contamination. The impact of this phenomenon can be
relieved using spatial division multiplexing, which refers to partitioning
users based on their spatial information and processing their signals
accordingly. The performance of such schemes depend primarily on the
implemented grouping method. In this paper, we propose a novel spatial grouping
scheme that aims at managing pilot contamination while reducing the required
training overhead in TDD massive MIMO. Herein, user specific decoding matrices
are derived based on the columns of the discrete Fourier transform matrix
(DFT), taken as a spatial basis. Copilot user groups are then formed in order
to obtain the best coverage of the spatial basis with minimum overlapping
between decoding matrices. We provide two algorithms that achieve the desired
grouping and derive their respective performance guarantees. We also address
inter-cell copilot interference through efficient pilot sequence allocation,
leveraging the formed copilot groups. Various numerical results are provided to
showcase the efficiency of the proposed algorithms.Comment: 30 pages, 6 figures, Submitted 201
A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems
In this paper, a novel covariance-based channel feedback mechanism is
investigated for frequency division duplexing (FDD) massive multi-input
multi-output (MIMO) systems. The concept capitalizes on the notion of user
statistical separability which was hinted in several prior works in the massive
antenna regime but not fully exploited so far. We here propose a hybrid
statistical-instantaneous feedback mechanism where the users are separated into
two classes of feedback design based on their channel covariance. Under the
hybrid framework, each user either operates on a statistical feedback mode or
quantized instantaneous channel feedback mode depending on their so-called
statistical isolability. The key challenge lies in the design of a
covariance-aware classification algorithm which can handle the complex mutual
interactions between all users. The classification is derived from rate bound
principles. A suitable precoding method is also devised under the mixed
statistical and instantaneous feedback model. Simulations are performed to
validate our analytical results and illustrate the sum rate advantages of the
proposed feedback scheme under a global feedback overhead constraint.Comment: 31 pages, 9 figure
Robust Geometry-Based User Scheduling for Large MIMO Systems Under Realistic Channel Conditions
The problem of user scheduling with reduced overhead of channel estimation in
the uplink of Massive multiple-input multiple-output (MIMO) systems has been
considered. A geometry-based stochastic channel model (GSCM), called the COST
2100 channel model has been used for realistic analysis of channels. In this
paper, we propose a new user selection algorithm based on knowledge of the
geometry of the service area and location of clusters, without having full
channel state information (CSI) at the base station (BS). The multi-user link
correlation in the GSCMs arises from the common clusters in the area. The
throughput depends on the position of clusters in the GSCMs and users in the
system. Simulation results show that although the BS does not require the
channel information of all users, by the proposed geometry-based user
scheduling algorithm the sum-rate of the system is only slightly less than the
well-known greedy weight clique scheme. Finally, the robustness of the proposed
algorithm to the inaccuracy of cluster localization is verified by the
simulation results.Comment: 4 figure
Low-Complexity Downlink User Selection for Massive MIMO Systems
In this paper we propose a pair of low-complexity user selection schemes with
zero-forcing precoding for multiuser massive MIMO downlink systems, in which
the base station is equipped with a large-scale antenna array. First, we derive
approximations of the ergodic sum rates of the systems invoking the
conventional random user selection (RUS) and the location-dependant user
selection (LUS). Then, the optimal number of simultaneously served user
equipments (UEs), , is investigated to maximize the sum rate
approximations. Upon exploiting , we develop two user selection schemes,
namely -RUS and -LUS, where UEs are selected either randomly or
based on their locations. Both of the proposed schemes are independent of the
instantaneous channel state information of small-scale fading, therefore
enjoying the same extremely-low computational complexity as that of the
conventional RUS scheme. Moreover, both of our proposed schemes achieve
significant sum rate improvement over the conventional RUS. In addition, it is
worth noting that like the conventional RUS, the -RUS achieves good
fairness among UEs.Comment: 11 pages, 27 figures, Accepted to publish on IEEE Systems Journal --
Special Issue on 5G Wireless Systems with Massive MIMO, Apr. 201
Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is
a newly introduced architecture that enables both spatial multiplexing and
beamforming while facilitating highly reconfigurable hardware implementation in
millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on
channel state information (CSI) acquisition and hybrid precoding. As benefited
from a coordinated and open-loop pilot beam pattern design, all the sub-arrays
can perform channel sounding with less training overhead compared with the
traditional orthogonal operation of each sub-array. Furthermore, two sparse
channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP)
and joint sparse Bayesian learning with reweighting (JSBL-),
are proposed to exploit the hidden structured sparsity in the beam-domain
channel vector. Finally, successive interference cancellation (SIC) based
hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO
system, which decomposes the joint sub-array RF beamformer design into an
interactive per-sub-array-group handle. Simulation results show that the
proposed two channel estimators fully take advantage of the partial coupling
characteristic of DPA-MIMO channels to perform channel recovery, and the
proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays
architecture with satisfactory performance and low complexity.Comment: accepted by IEEE Transactions on Vehicular Technolog
Heterogeneous Doppler Spread-based CSI Estimation Planning for TDD Massive MIMO
Massive multi-input multi-output (Massive MIMO) has been recognized as a key
technology to meet the demand for higher data capacity and massive
connectivity. Nevertheless, the number of active users is restricted due to
training overhead and the limited coherence time. Current wireless systems
assume the same coherence slot duration for all users, regardless of their
heterogeneous Doppler spreads. In this paper, we exploit this neglected degree
of freedom in addressing the training overhead bottleneck. We propose a new
uplink training scheme where the periodicity of pilot transmission differs
among users based on their actual channel coherence times. Since the changes in
the wireless channel are, primarily, due to movement, uplink training decisions
are optimized, over long time periods, while considering the evolution of the
users channels and locations. Owing to the different rates of the wireless
channel and location evolution, a two time scale control problem is formulated.
In the fast time scale, an optimal training policy is derived by choosing which
users are requested to send their pilots. In the slow time scale, location
estimation decisions are optimized. Simulation results show that the derived
training policies provide a considerable improvement of the cumulative average
spectral efficiency even with partial location knowledge.Comment: 30 pages, 5 figures, Submitted 201
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