65 research outputs found
SWIPT in Mixed Near- and Far-Field Channels: Joint Beam Scheduling and Power Allocation
Extremely large-scale array (XL-array) has emerged as a promising technology
to enhance the spectrum efficiency and spatial resolution in future wireless
networks by exploiting massive number of antennas for generating pencil-like
beamforming. This also leads to a fundamental paradigm shift from conventional
far-field communications towards the new near-field communications. In contrast
to the existing works that mostly considered simultaneous wireless information
and power transfer (SWIPT) in the far field, we consider in this paper a new
and practical scenario, called mixed near- and far-field SWIPT, where energy
harvesting (EH) and information decoding (ID) receivers are located in the
near- and far-field regions of the XL-array base station (BS), respectively.
Specifically, we formulate an optimization problem to maximize the weighted
sum-power harvested at all EH receivers by jointly designing the BS beam
scheduling and power allocation, under the constraints on the maximum sum-rate
and BS transmit power. First, for the general case with multiple EH and ID
receivers, we propose an efficient algorithm to obtain a suboptimal solution by
utilizing the binary variable elimination and successive convex approximation
methods. To obtain useful insights, we then study the joint design for special
cases. In particular, we show that when there are multiple EH receivers and one
ID receiver, in most cases, the optimal design is allocating a portion of power
to the ID receiver for satisfying the rate constraint, while the remaining
power is allocated to one EH receiver with the highest EH capability. This is
in sharp contrast to the conventional far-field SWIPT case, for which all
powers should be allocated to ID receivers. Numerical results show that our
proposed joint design significantly outperforms other benchmark schemes without
the optimization of beam scheduling and/or power allocation.Comment: In this paper, we consider a new scenario of mixed-field SWIPT, and
studied efficient beam scheduling and power allocation. The paper is accepted
to JSAC. arXiv admin note: substantial text overlap with arXiv:2304.0794
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
To overcome the limited coverage in traditional wireless sensor networks,
\emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To
achieve longer battery lives of user devices and incentive human involvement,
this paper presents a novel approach that seamlessly integrates MCS with
wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS),
for supporting crowd sensing with energy consumption and offering rewards as
incentives. The optimization problem is formulated to simultaneously maximize
the data utility and minimize the energy consumption for service operator, by
jointly controlling wireless-power allocation at the \emph{access point} (AP)
as well as sensing-data size, compression ratio, and sensor-transmission
duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the
optimal power allocation policy is shown to have a \emph{threshold}-based
structure with respect to a defined \emph{crowd-sensing priority} function for
each MS. Given fixed sensing-data utilities, the compression policy achieves
the optimal compression ratio. Extensive simulations are also presented to
verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206
Double-Active-IRS Aided Wireless Communication: Deployment Optimization and Capacity Scaling
In this letter, we consider a double-active-intelligent reflecting surface
(IRS) aided wireless communication system, where two active IRSs are properly
deployed to assist the communication from a base station (BS) to multiple users
located in a given zone via the double-reflection links. Under the assumption
of fixed per-element amplification power for each active-IRS element, we
formulate a rate maximization problem subject to practical constraints on the
reflection design, elements allocation, and placement of active IRSs. To solve
this non-convex problem, we first obtain the optimal active-IRS reflections and
BS beamforming, based on which we then jointly optimize the active-IRS elements
allocation and placement by using the alternating optimization (AO) method.
Moreover, we show that given the fixed per-element amplification power, the
received signal-to-noise ratio (SNR) at the user increases asymptotically with
the square of the number of reflecting elements; while given the fixed number
of reflecting elements, the SNR does not increase with the per-element
amplification power when it is asymptotically large. Last, numerical results
are presented to validate the effectiveness of the proposed AO-based algorithm
and compare the rate performance of the considered double-active-IRS aided
wireless system with various benchmark systems
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