3 research outputs found
Towards Reconfigurable Intelligent Surfaces Powered Green Wireless Networks
The adoption of reconfigurable intelligent surface (RIS) in wireless networks
can enhance the spectrum- and energy-efficiency by controlling the propagation
environment. Although the RIS does not consume any transmit power, the circuit
power of the RIS cannot be ignored, especially when the number of reflecting
elements is large. In this paper, we propose the joint design of beamforming
vectors at the base station, active RIS set, and phase-shift matrices at the
active RISs to minimize the network power consumption, including the RIS
circuit power consumption, while taking into account each user's target data
rate requirement and each reflecting element's constant modulus constraint.
However, the formulated problem is a mixed-integer quadratic programming (MIQP)
problem, which is NP-hard. To this end, we present an alternating optimization
method, which alternately solves second order cone programming (SOCP) and MIQP
problems to update the optimization variables. Specifically, the MIQP problem
is further transformed into a semidefinite programming problem by applying
binary relaxation and semidefinite relaxation. Finally, an efficient algorithm
is developed to solve the problem. Simulation results show that the proposed
algorithm significantly reduces the network power consumption and reveal the
importance of taking into account the RIS circuit power consumption.Comment: Accepted by IEEE Wireless Communications and Networking Conference
(WCNC) 202
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
The ever-growing popularity and rapid improving of artificial intelligence
(AI) have raised rethinking on the evolution of wireless networks. Mobile edge
computing (MEC) provides a natural platform for AI applications since it is
with rich computation resources to train machine learning (ML) models, as well
as low-latency access to the data generated by mobile and internet of things
(IoT) devices. In this paper, we present an infrastructure to perform ML tasks
at an MEC server with the assistance of a reconfigurable intelligent surface
(RIS). In contrast to conventional communication systems where the principal
criterions are to maximize the throughput, we aim at maximizing the learning
performance. Specifically, we minimize the maximum learning error of all
participating users by jointly optimizing transmit power of mobile users,
beamforming vectors of the base station (BS), and the phase-shift matrix of the
RIS. An alternating optimization (AO)-based framework is proposed to optimize
the three terms iteratively, where a successive convex approximation
(SCA)-based algorithm is developed to solve the power allocation problem,
closed-form expressions of the beamforming vectors are derived, and an
alternating direction method of multipliers (ADMM)-based algorithm is designed
together with an error level searching (ELS) framework to effectively solve the
challenging nonconvex optimization problem of the phase-shift matrix.
Simulation results demonstrate significant gains of deploying an RIS and
validate the advantages of our proposed algorithms over various benchmarks.
Lastly, a unified communication-training-inference platform is developed based
on the CARLA platform and the SECOND network, and a use case (3D object
detection in autonomous driving) for the proposed scheme is demonstrated on the
developed platform.Comment: 30 pages, 8 figures, submitted to IEEE Transactions on Cognitive
Communications and Networkin
Reconfigurable Intelligent Surface for Green Edge Inference
Reconfigurable intelligent surface (RIS) as an emerging cost-effective
technology can enhance the spectrum- and energy-efficiency of wireless
networks. In this paper, we consider an RIS-aided green edge inference system,
where the inference tasks generated from resource-limited mobile devices (MDs)
are uploaded to and cooperatively performed at multiple resource-enhanced base
stations (BSs). Taking into account both the computation and uplink/downlink
transmit power consumption, we formulate an overall network power consumption
minimization problem, which calls for the joint design of the set of tasks
performed by each BS, transmit and receive beamforming vectors of the BSs,
transmit power of the MDs, and uplink/downlink phase-shift matrices at the RIS.
Such a problem is a mixed combinatorial optimization problem with nonconvex
constraints and is highly intractable. To address the challenge of the
combinatorial objective, a group sparse reformulation is proposed by exploiting
the group sparsity structure of the beamforming vectors, while a
block-structured optimization (BSO) approach is proposed to decouple the
optimization variables. Finally, we propose a BSO with mixed -norm
and difference-of-convex-functions (DC) based three-stage framework to solve
the problem, where the mixed -norm is adopted to induce the group
sparsity of beamforming vectors and DC is adopted to effectively handle the
nonconvex rank-one constraint after matrix lifting. Numerical results
demonstrate the supreme gain of deploying an RIS and confirm the effectiveness
of the proposed algorithm over the baseline algorithms.Comment: This is the first attempt to unify beamforming vectors, transmit
power, and phase shifts design in both the uplink and downlink transmissions
into a general framework. This work will be submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl