1,752 research outputs found
Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer
This paper studies a wireless-energy-transfer (WET) enabled massive
multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid
data-and-energy access point (H-AP) and multiple single-antenna users. In the
WET-MM system, the H-AP is equipped with a large number of antennas and
functions like a conventional AP in receiving data from users, but additionally
supplies wireless power to the users. We consider frame-based transmissions.
Each frame is divided into three phases: the uplink channel estimation (CE)
phase, the downlink WET phase, as well as the uplink wireless information
transmission (WIT) phase. Firstly, users use a fraction of the previously
harvested energy to send pilots, while the H-AP estimates the uplink channels
and obtains the downlink channels by exploiting channel reciprocity. Next, the
H-AP utilizes the channel estimates just obtained to transfer wireless energy
to all users in the downlink via energy beamforming. Finally, the users use a
portion of the harvested energy to send data to the H-AP simultaneously in the
uplink (reserving some harvested energy for sending pilots in the next frame).
To optimize the throughput and ensure rate fairness, we consider the problem of
maximizing the minimum rate among all users. In the large- regime, we obtain
the asymptotically optimal solutions and some interesting insights for the
optimal design of WET-MM system. We define a metric, namely, the massive MIMO
degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by
. We show that the proposed WET-MM system is optimal in terms of
MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.Comment: 15 double-column pages, 6 figures, 1 table, to appear in IEEE JSAC in
February 2015, special issue on wireless communications powered by energy
harvesting and wireless energy transfe
Optimized Training Design for Wireless Energy Transfer
Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising
solution to provide cost-effective and reliable power supplies for
energy-constrained wireless networks, has drawn growing interests recently. To
overcome the significant propagation loss over distance, employing
multi-antennas at the energy transmitter (ET) to more efficiently direct
wireless energy to desired energy receivers (ERs), termed \emph{energy
beamforming}, is an essential technique for enabling WET. However, the
achievable gain of energy beamforming crucially depends on the available
channel state information (CSI) at the ET, which needs to be acquired
practically. In this paper, we study the design of an efficient channel
acquisition method for a point-to-point multiple-input multiple-output (MIMO)
WET system by exploiting the channel reciprocity, i.e., the ET estimates the
CSI via dedicated reverse-link training from the ER. Considering the limited
energy availability at the ER, the training strategy should be carefully
designed so that the channel can be estimated with sufficient accuracy, and yet
without consuming excessive energy at the ER. To this end, we propose to
maximize the \emph{net} harvested energy at the ER, which is the average
harvested energy offset by that used for channel training. An optimization
problem is formulated for the training design over MIMO Rician fading channels,
including the subset of ER antennas to be trained, as well as the training time
and power allocated. Closed-form solutions are obtained for some special
scenarios, based on which useful insights are drawn on when training should be
employed to improve the net transferred energy in MIMO WET systems.Comment: 30 pages, 9 figures, to appear in IEEE Trans. on Communication
Wireless Information and Power Transfer in Full-Duplex Systems with Massive Antenna Arrays
We consider a multiuser wireless system with a full-duplex hybrid access
point (HAP) that transmits to a set of users in the downlink channel, while
receiving data from a set of energy-constrained sensors in the uplink channel.
We assume that the HAP is equipped with a massive antenna array, while all
users and sensor nodes have a single antenna. We adopt a time-switching
protocol where in the first phase, sensors are powered through wireless energy
transfer from HAP and HAP estimates the downlink channel of the users. In the
second phase, sensors use the harvested energy to transmit to the HAP. The
downlink-uplink sum-rate region is obtained by solving downlink sum-rate
maximization problem under a constraint on uplink sum-rate. Moreover, assuming
perfect and imperfect channel state information, we derive expressions for the
achievable uplink and downlink rates in the large-antenna limit and approximate
results that hold for any finite number of antennas. Based on these analytical
results, we obtain the power-scaling law and analyze the effect of the number
of antennas on the cancellation of intra-user interference and the
self-interference.Comment: Accepted for the IEEE International Conference on Communications (ICC
2017
Wireless Power Transfer in Massive MIMO Aided HetNets with User Association
This paper explores the potential of wireless power transfer (WPT) in massive
multiple input multiple output (MIMO) aided heterogeneous networks (HetNets),
where massive MIMO is applied in the macrocells, and users aim to harvest as
much energy as possible and reduce the uplink path loss for enhancing their
information transfer. By addressing the impact of massive MIMO on the user
association, we compare and analyze two user association schemes. We adopt the
linear maximal ratio transmission beam-forming for massive MIMO power transfer
to recharge users. By deriving new statistical properties, we obtain the exact
and asymptotic expressions for the average harvested energy. Then we derive the
average uplink achievable rate under the harvested energy constraint.Comment: 36 pages, 11 figures, to appear in IEEE Transactions on
Communication
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