658 research outputs found
Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems
This paper considers pilot design to mitigate pilot contamination and provide
good service for everyone in multi-cell Massive multiple input multiple output
(MIMO) systems. Instead of modeling the pilot design as a combinatorial
assignment problem, as in prior works, we express the pilot signals using a
pilot basis and treat the associated power coefficients as continuous
optimization variables. We compute a lower bound on the uplink capacity for
Rayleigh fading channels with maximum ratio detection that applies with
arbitrary pilot signals. We further formulate the max-min fairness problem
under power budget constraints, with the pilot signals and data powers as
optimization variables. Because this optimization problem is non-deterministic
polynomial-time hard due to signomial constraints, we then propose an algorithm
to obtain a local optimum with polynomial complexity. Our framework serves as a
benchmark for pilot design in scenarios with either ideal or non-ideal
hardware. Numerical results manifest that the proposed optimization algorithms
are close to the optimal solution obtained by exhaustive search for different
pilot assignments and the new pilot structure and optimization bring large
gains over the state-of-the-art suboptimal pilot design.Comment: 16 pages, 8 figures. Accepted to publish at IEEE Transactions on
Wireless Communication
Max-Min SINR in Large-Scale Single-Cell MU-MIMO: Asymptotic Analysis and Low Complexity Transceivers
This work focuses on the downlink and uplink of large-scale single-cell
MU-MIMO systems in which the base station (BS) endowed with antennas
communicates with single-antenna user equipments (UEs). Particularly, we
aim at reducing the complexity of the linear precoder and receiver that
maximize the minimum signal-to-interference-plus-noise ratio subject to a given
power constraint. To this end, we consider the asymptotic regime in which
and grow large with a given ratio. Tools from random matrix theory (RMT)
are then used to compute, in closed form, accurate approximations for the
parameters of the optimal precoder and receiver, when imperfect channel state
information (modeled by the generic Gauss-Markov formulation form) is available
at the BS. The asymptotic analysis allows us to derive the asymptotically
optimal linear precoder and receiver that are characterized by a lower
complexity (due to the dependence on the large scale components of the channel)
and, possibly, by a better resilience to imperfect channel state information.
However, the implementation of both is still challenging as it requires fast
inversions of large matrices in every coherence period. To overcome this issue,
we apply the truncated polynomial expansion (TPE) technique to the precoding
and receiving vector of each UE and make use of RMT to determine the optimal
weighting coefficients on a per-UE basis that asymptotically solve the max-min
SINR problem. Numerical results are used to validate the asymptotic analysis in
the finite system regime and to show that the proposed TPE transceivers
efficiently mimic the optimal ones, while requiring much lower computational
complexity.Comment: 13 pages, 4 figures, submitted to IEEE Transactions on Signal
Processin
Optimal Linear Precoding for Indoor Visible Light Communication System
Visible light communication (VLC) is an emerging technique that uses
light-emitting diodes (LED) to combine communication and illumination. It is
considered as a promising scheme for indoor wireless communication that can be
deployed at reduced costs while offering high data rate performance. In this
paper, we focus on the design of the downlink of a multi-user VLC system.
Inherent to multi-user systems is the interference caused by the broadcast
nature of the medium. Linear precoding based schemes are among the most popular
solutions that have recently been proposed to mitigate inter-user interference.
This paper focuses on the design of the optimal linear precoding scheme that
solves the max-min signal-to-interference-plus-noise ratio (SINR) problem. The
performance of the proposed precoding scheme is studied under different working
conditions and compared with the classical zero-forcing precoding. Simulations
have been provided to illustrate the high gain of the proposed scheme.Comment: 5 pages, 4 figures, accepted for publication in ICC proceedings 201
Resource Allocation for Multiple-Input and Multiple-Output Interference Networks
To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed.
The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows.
It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form.
A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in
energy and/or spectral efficiency to wireless systems due to the greatly
improved spatial resolution and array gain. Recent works in the field of
massive multiple-input multiple-output (MIMO) show that the user channels
decorrelate when the number of antennas at the base stations (BSs) increases,
thus strong signal gains are achievable with little inter-user interference.
Since these results rely on asymptotics, it is important to investigate whether
the conventional system models are reasonable in this asymptotic regime. This
paper considers a new system model that incorporates general transceiver
hardware impairments at both the BSs (equipped with large antenna arrays) and
the single-antenna user equipments (UEs). As opposed to the conventional case
of ideal hardware, we show that hardware impairments create finite ceilings on
the channel estimation accuracy and on the downlink/uplink capacity of each UE.
Surprisingly, the capacity is mainly limited by the hardware at the UE, while
the impact of impairments in the large-scale arrays vanishes asymptotically and
inter-user interference (in particular, pilot contamination) becomes
negligible. Furthermore, we prove that the huge degrees of freedom offered by
massive MIMO can be used to reduce the transmit power and/or to tolerate larger
hardware impairments, which allows for the use of inexpensive and
energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/massive-MIMO-hardware-impairment
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