123 research outputs found
Max-min Fair Beamforming for SWIPT Systems with Non-linear EH Model
We study the beamforming design for multiuser systems with simultaneous
wireless information and power transfer (SWIPT). Employing a practical
non-linear energy harvesting (EH) model, the design is formulated as a
non-convex optimization problem for the maximization of the minimum harvested
power across several energy harvesting receivers. The proposed problem
formulation takes into account imperfect channel state information (CSI) and a
minimum required signal-to-interference-plus-noise ratio (SINR). The globally
optimal solution of the design problem is obtained via the semidefinite
programming (SDP) relaxation approach. Interestingly, we can show that at most
one dedicated energy beam is needed to achieve optimality. Numerical results
demonstrate that with the proposed design a significant performance gain and
improved fairness can be provided to the users compared to two baseline
schemes.Comment: Invited paper, IEEE VTC 2017, Fall, Toronto, Canad
Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks under Fairness Constraints
In this paper, we study the secrecy throughput of a full-duplex wireless
powered communication network (WPCN) for internet of things (IoT). The WPCN
consists of a full-duplex multi-antenna base station (BS) and a number of
sensor nodes. The BS transmits energy all the time, and each node harvests
energy prior to its transmission time slot. The nodes sequentially transmit
their confidential information to the BS, and the other nodes are considered as
potential eavesdroppers. We first formulate the sum secrecy throughput
optimization problem of all the nodes. The optimization variables are the
duration of the time slots and the BS beamforming vectors in different time
slots. The problem is shown to be non-convex. To tackle the problem, we propose
a suboptimal two stage approach, referred to as sum secrecy throughput
maximization (SSTM). In the first stage, the BS focuses its beamforming to
blind the potential eavesdroppers (other nodes) during information transmission
time slots. Then, the optimal beamforming vector in the initial non-information
transmission time slot and the optimal time slots are derived. We then consider
fairness among the nodes and propose max-min fair (MMF) and proportional fair
(PLF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of
the nodes, while the PLF tries to achieve a good trade-off between the sum
secrecy throughput and fairness among the nodes. Through numerical simulations,
we first demonstrate the superior performance of the SSTM to uniform time
slotting and beamforming in different settings. Then, we show the effectiveness
of the proposed fair algorithms
Energy Efficiency Optimization for a Multiuser IRS-aided MISO System with SWIPT
Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes
Beamforming Optimization for Active Intelligent Reflecting Surface-Aided SWIPT
In this paper, we study an active IRS-aided simultaneous wireless information
and power transfer (SWIPT) system. Specifically, an active IRS is deployed to
assist a multi-antenna access point (AP) to convey information and energy
simultaneously to multiple single-antenna information users (IUs) and energy
users (EUs). Two joint transmit and reflect beamforming optimization problems
are investigated with different practical objectives. The first problem
maximizes the weighted sum-power harvested by the EUs subject to individual
signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while
the second problem maximizes the weighted sum-rate of the IUs subject to
individual energy harvesting (EH) constraints at the EUs. The optimization
problems are non-convex and difficult to solve optimally. To tackle these two
problems, we first rigorously prove that dedicated energy beams are not
required for their corresponding semidefinite relaxation (SDR) reformulations
and the SDR is tight for the first problem, thus greatly simplifying the AP
precoding design. Then, by capitalizing on the techniques of alternating
optimization (AO), SDR, and successive convex approximation (SCA),
computationally efficient algorithms are developed to obtain suboptimal
solutions of the resulting optimization problems. Simulation results
demonstrate that, given the same total system power budget, significant
performance gains in terms of operating range of wireless power transfer (WPT),
total harvested energy, as well as achievable rate can be obtained by our
proposed designs over benchmark schemes (especially the one adopting a passive
IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the
users for the effective operation of WPT/SWIPT.Comment: 32 pages, 10 figures, submitted to IEEE journal for possible
publicatio
Spectral, Energy and Computation Efficiency in Future 5G Wireless Networks
Wireless technology has revolutionized the way people communicate. From first generation, or 1G, in the 1980s to current, largely deployed 4G in the 2010s, we have witnessed not only a technological leap, but also the reformation of associated applications. It is expected that 5G will become commercially available in 2020. 5G is driven by ever-increasing demands for high mobile traffic, low transmission delay, and massive numbers of connected devices. Today, with the popularity of smart phones, intelligent appliances, autonomous cars, and tablets, communication demands are higher than ever, especially when it comes to low-cost and easy-access solutions.
Existing communication architecture cannot fulfill 5G’s needs. For example, 5G requires connection speeds up to 1,000 times faster than current technology can provide. Also, from transmitter side to receiver side, 5G delays should be less than 1ms, while 4G targets a 5ms delay speed. To meet these requirements, 5G will apply several disruptive techniques. We focus on two of them: new radio and new scheme. As for the former, we study the non-orthogonal multiple access (NOMA) and as for the latter, we use mobile edge computing (MEC).
Traditional communication systems allow users to communicate alternatively, which clearly avoids inter-user interference, but also caps the connection speed. NOMA, on the other hand, allows multiple users to transmit simultaneously. While NOMA will inevitably cause excessive interference, we prove such interference can be mitigated by an advanced receiver side technique. NOMA has existed on the research frontier since 2013. Since that time, both academics and industry professionals have extensively studied its performance. In this dissertation, our contribution is to incorporate NOMA with several potential schemes, such as relay, IoT, and cognitive radio networks. Furthermore, we reviewed various limitations on NOMA and proposed a more practical model.
In the second part, MEC is considered. MEC is a transformation from the previous cloud computing system. In particular, MEC leverages powerful devices nearby and instead of sending information to distant cloud servers, the transmission occurs in closer range, which can effectively reduce communication delay. In this work, we have proposed a new evaluation metric for MEC which can more effectively leverage the trade-off between the amount of computation and the energy consumed thereby.
A practical communication system for wearable devices is proposed in the last part, which combines all the techniques discussed above. The challenges for wearable communication are inherent in its diverse needs, as some devices may require low speed but high reliability (factory sensors), while others may need low delay (medical devices). We have addressed these challenges and validated our findings through simulations
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