3,622 research outputs found
Massive MIMO and Small Cells: Improving Energy Efficiency by Optimal Soft-Cell Coordination
To improve the cellular energy efficiency, without sacrificing
quality-of-service (QoS) at the users, the network topology must be densified
to enable higher spatial reuse. We analyze a combination of two densification
approaches, namely "massive" multiple-input multiple-output (MIMO) base
stations and small-cell access points. If the latter are operator-deployed, a
spatial soft-cell approach can be taken where the multiple transmitters serve
the users by joint non-coherent multiflow beamforming. We minimize the total
power consumption (both dynamic emitted power and static hardware power) while
satisfying QoS constraints. This problem is proved to have a hidden convexity
that enables efficient solution algorithms. Interestingly, the optimal solution
promotes exclusive assignment of users to transmitters. Furthermore, we provide
promising simulation results showing how the total power consumption can be
greatly improved by combining massive MIMO and small cells; this is possible
with both optimal and low-complexity beamforming.Comment: Published at International Conference on Telecommunications (ICT
2013), 6-8 May 2013, Casablanca, Morocco, 5 pages, 4 figures, 2 tables. This
version includes the Matlab code necessary to reproduce the simulations; see
the ancillary files. This version also corrects errors in Table 1 and in the
simulations, which affected Figs. 3-
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Backscatter communication (BSC) is being realized as the core technology for
pervasive sustainable Internet-of-Things applications. However, owing to the
resource-limitations of passive tags, the efficient usage of multiple antennas
at the reader is essential for both downlink excitation and uplink detection.
This work targets at maximizing the achievable sum-backscattered-throughput by
jointly optimizing the transceiver (TRX) design at the reader and
backscattering coefficients (BC) at the tags. Since, this joint problem is
nonconvex, we first present individually-optimal designs for the TRX and BC. We
show that with precoder and {combiner} designs at the reader respectively
targeting downlink energy beamforming and uplink Wiener filtering operations,
the BC optimization at tags can be reduced to a binary power control problem.
Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low
and high signal-to-noise-ratio regimes. Based on these developments, an
iterative low-complexity algorithm is proposed to yield an efficient
jointly-suboptimal design. Thereafter, we discuss the practical utility of the
proposed designs to other application settings like wireless powered
communication networks and BSC with imperfect channel state information.
Lastly, selected numerical results, validating the analysis and shedding novel
insights, demonstrate that the proposed designs can yield significant
enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on
Communication
Energy-Efficient Coordinated Multi-Cell Multigroup Multicast Beamforming with Antenna Selection
This paper studies energy-efficient coordinated beamforming in multi-cell
multi-user multigroup multicast multiple-input single-output systems. We aim at
maximizing the network energy efficiency by taking into account the fact that
some of the radio frequency chains can be switched off in order to save power.
We consider the antenna specific maximum power constraints to avoid non-linear
distortion in power amplifiers and user-specific quality of service (QoS)
constraints to guarantee a certain QoS levels. We first introduce binary
antenna selection variables and use the perspective formulation to model the
relation between them and the beamformers. Subsequently, we propose a new
formulation which reduces the feasible set of the continuous relaxation,
resulting in better performance compared to the original perspective
formulation based problem. However, the resulting optimization problem is a
mixed-Boolean non-convex fractional program, which is difficult to solve. We
follow the standard continuous relaxation of the binary antenna selection
variables, and then reformulate the problem such that it is amendable to
successive convex approximation. Thereby, solving the continuous relaxation
mostly results in near-binary solution. To recover the binary variables from
the continuous relaxation, we switch off all the antennas for which the
continuous values are smaller than a small threshold. Numerical results
illustrate the superior convergence result and significant achievable gains in
terms of energy efficiency with the proposed algorithm.Comment: 6 pages, 5 figures, accepted to IEEE ICC 2017 - International
Workshop on 5G RAN Desig
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