152 research outputs found
Fundamental Limits of Intelligent Reflecting Surface Aided Multiuser Broadcast Channel
Intelligent reflecting surface (IRS) has recently received significant
attention in wireless networks owing to its ability to smartly control the
wireless propagation through passive reflection. Although prior works have
employed the IRS to enhance the system performance under various setups, the
fundamental capacity limits of an IRS aided multi-antenna multi-user system
have not yet been characterized. Motivated by this, we investigate an IRS aided
multiple-input single-output (MISO) broadcast channel by considering the
capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming
configurations. We first propose a bisection based framework to characterize
its capacity region by optimally solving the sum-rate maximization problem
under a set of rate constraints, which is also applicable to characterize the
achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is
rigorously proved that dynamic beamforming is able to enlarge the achievable
rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal
channels, whereas the attained gains become marginal due to the reduction of
the channel correlations induced by smartly adjusting the IRS phase-shifts. The
result implies that employing the IRS is able to reduce the demand for
implementing dynamic beamforming. Finally, we analytically prove that the
sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS
aided DPC with a sufficiently large IRS in practice. Simulation results shed
light on the impact of the IRS on transceiver designs and validate our
theoretical findings, which provide useful guidelines to practical systems by
indicating that replacing sophisticated schemes with easy-implementation
schemes would only result in slight performance loss
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
Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks
publisher: Elsevier articletitle: Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks journaltitle: Ad Hoc Networks articlelink: http://dx.doi.org/10.1016/j.adhoc.2016.08.005 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved
Downlink Noncoherent Cooperation without Transmitter Phase Alignment
Multicell joint processing can mitigate inter-cell interference and thereby
increase the spectral efficiency of cellular systems. Most previous work has
assumed phase-aligned (coherent) transmissions from different base transceiver
stations (BTSs), which is difficult to achieve in practice. In this work, a
noncoherent cooperative transmission scheme for the downlink is studied, which
does not require phase alignment. The focus is on jointly serving two users in
adjacent cells sharing the same resource block. The two BTSs partially share
their messages through a backhaul link, and each BTS transmits a superposition
of two codewords, one for each receiver. Each receiver decodes its own message,
and treats the signals for the other receiver as background noise. With
narrowband transmissions the achievable rate region and maximum achievable
weighted sum rate are characterized by optimizing the power allocation (and the
beamforming vectors in the case of multiple transmit antennas) at each BTS
between its two codewords. For a wideband (multicarrier) system, a dual
formulation of the optimal power allocation problem across sub-carriers is
presented, which can be efficiently solved by numerical methods. Results show
that the proposed cooperation scheme can improve the sum rate substantially in
the low to moderate signal-to-noise ratio (SNR) range.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Optimized Transmission with Improper Gaussian Signaling in the K-User MISO Interference Channel
This paper studies the achievable rate region of the K-user Gaussian
multiple-input single-output interference channel (MISO-IC) with the
interference treated as noise, when improper or circularly asymmetric complex
Gaussian signaling is applied. The transmit optimization with improper Gaussian
signaling involves not only the signal covariance matrix as in the conventional
proper or circularly symmetric Gaussian signaling, but also the signal
pseudo-covariance matrix, which is conventionally set to zero in proper
Gaussian signaling. By exploiting the separable rate expression with improper
Gaussian signaling, we propose a separate transmit covariance and
pseudo-covariance optimization algorithm, which is guaranteed to improve the
users' achievable rates over the conventional proper Gaussian signaling. In
particular, for the pseudo-covariance optimization, we establish the optimality
of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit
covariance matrices for achieving the Pareto boundary of the rate region. Based
on this result, we are able to greatly reduce the number of variables in the
pseudo-covariance optimization problem and thereby develop an efficient
solution by applying the celebrated semidefinite relaxation (SDR) technique.
Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC)
with improper Gaussian signaling or so-called widely linear transmit precoding.Comment: 27 pages, 5 figures, 2 table
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