538 research outputs found
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems
Large-scale MIMO systems can yield a substantial improvement in spectral
efficiency for future communication systems. Due to the finer spatial
resolution achieved by a huge number of antennas at the base stations, these
systems have shown to be robust to inter-user interference and the use of
linear precoding is asymptotically optimal. However, most precoding schemes
exhibit high computational complexity as the system dimensions increase. For
example, the near-optimal RZF requires the inversion of a large matrix. This
motivated our companion paper, where we proposed to solve the issue in
single-cell multi-user systems by approximating the matrix inverse by a
truncated polynomial expansion (TPE), where the polynomial coefficients are
optimized to maximize the system performance. We have shown that the proposed
TPE precoding with a small number of coefficients reaches almost the
performance of RZF but never exceeds it. In a realistic multi-cell scenario
involving large-scale multi-user MIMO systems, the optimization of RZF
precoding has thus far not been feasible. This is mainly attributed to the high
complexity of the scenario and the non-linear impact of the necessary
regularizing parameters. On the other hand, the scalar weights in TPE precoding
give hope for possible throughput optimization. Following the same methodology
as in the companion paper, we exploit random matrix theory to derive a
deterministic expression for the asymptotic SINR for each user. We also provide
an optimization algorithm to approximate the weights that maximize the
network-wide weighted max-min fairness. The optimization weights can be used to
mimic the user throughput distribution of RZF precoding. Using simulations, we
compare the network throughput of the TPE precoding with that of the suboptimal
RZF scheme and show that our scheme can achieve higher throughput using a TPE
order of only 3
An Analytical Framework for Heterogeneous Partial Feedback Design in Heterogeneous Multicell OFDMA Networks
The inherent heterogeneous structure resulting from user densities and large
scale channel effects motivates heterogeneous partial feedback design in
heterogeneous networks. In such emerging networks, a distributed scheduling
policy which enjoys multiuser diversity as well as maintains fairness among
users is favored for individual user rate enhancement and guarantees. For a
system employing the cumulative distribution function based scheduling, which
satisfies the two above mentioned desired features, we develop an analytical
framework to investigate heterogeneous partial feedback in a general
OFDMA-based heterogeneous multicell employing the best-M partial feedback
strategy. Exact sum rate analysis is first carried out and closed form
expressions are obtained by a novel decomposition of the probability density
function of the selected user's signal-to-interference-plus-noise ratio. To
draw further insight, we perform asymptotic analysis using extreme value theory
to examine the effect of partial feedback on the randomness of multiuser
diversity, show the asymptotic optimality of best-1 feedback, and derive an
asymptotic approximation for the sum rate in order to determine the minimum
required partial feedback.Comment: To appear in IEEE Trans. on Signal Processin
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
Fundamental Limits in MIMO Broadcast Channels
This paper studies the fundamental limits of MIMO broadcast channels from a high level, determining the sum-rate capacity of the system as a function of system paramaters, such as the number of transmit antennas, the number of users, the number of receive antennas, and the total transmit power. The crucial role of channel state information at the transmitter is emphasized, as well as the emergence of opportunistic transmission schemes. The effects of channel estimation errors, training, and spatial correlation are studied, as well as issues related to fairness, delay and differentiated rate scheduling
- …