Wireless powered cooperation-assisted mobile edge computing

This paper studies a mobile edge computing (MEC) system in which two mobile devices are energized by the wireless power transfer (WPT) from an access point (AP) and they can offload part or all of their computation-intensive latency-critical tasks to the AP connected with an MEC server or an edge cloud. This harvest-then-offload protocol operates in an optimized time-division manner. To overcome the doubly near-far effect for the farther mobile device, cooperative communications in the form of relaying via the nearer mobile device is considered for offloading. Our aim is to minimize the AP's total transmit energy subject to the constraints of the computational tasks. We illustrate that the optimization is equivalent to a min-max problem, which can be optimally solved by a two-phase method. The first phase obtains the optimal offloading decisions by solving a sum-energy-saving maximization problem for given an energy transmit power. In the second phase, the optimal minimum energy transmit power is obtained by a bisection search method. Numerical results demonstrate that the optimized MEC system utilizing cooperation has significant performance improvement over systems without cooperation

Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Towards Energy Sustainability

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design

Unary Coding Design for Simultaneous Wireless Information and Power Transfer with Practical M-QAM

Relying on the propagation of modulated radio-frequency (RF) signals, we can achieve simultaneous wireless information and power transfer (SWIPT) to support low-power communication devices. In this paper, we proposed a unary coding based SWIPT encoder by considering a practical M-QAM. Markov chains are exploited for characterising coherent binary information source and for modelling the generation process of modulated symbols. Therefore, both mutual information and the average energy harvesting performance at the SWIPT receiver are analysed in semi-closed-form. With the aid of the genetic algorithm, the sub-optimal codeword distribution of the coded information source is obtained by maximising the average energy harvesting performance, while satisfying the requirement of the mutual information. Simulation results demonstrate the advantage of the SWIPT encoder. Moreover, a higher-level unary code and a lower-order M-QAM results in higher WPT performance, when the maximum transmit power of the modulated symbol is fixed

Wideband Waveforming for Integrated Data and Energy Transfer: Creating Extra Gain Beyond Multiple Antennas and Multiple Carriers

When wideband signals propagate in a rich-scatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by carefully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrated-data-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fair-throughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modified quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimum-mean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequency-division-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-single-output orthogonal-frequency-division-multiplex-access (MISO-OFDMA)

Secrecy Energy Efficiency in Wireless Powered Heterogeneous Networks: A Distributed ADMM Approach

OAPA This paper investigates the physical layer security in heterogeneous networks (HetNets) supported by simultaneous wireless information and power transfer (SWIPT). We first consider a two-tier HetNet composed of a macrocell and several femtocells, where the macrocell base station (BS) serves multiple users in the presence of a malicious eavesdropper, while each femtocell BS serves a couple of Internet-of-things (IoT) users. With regard to the energy constraint of IoT users, SWIPT is performed at the femtocell BSs, and IoT users accomplish the reception of information and energy in a time-switching (TS) manner, where information secrecy is to be protected. To enhance the secrecy performance, we inject artificial noise (AN) into the transmit beam at both macrocell and femtocell BSs, and for the sake of achieving green communications, we formulate the problem of maximizing secrecy energy efficiency while considering the fairness in a cross-tier multi-cell coordinated beamforming (MCBF) design. To handle this resulting nonconvex max-min fractional program problem, we propose an iterative algorithm by applying successive convex approximation method. Then, we further develop a decentralized solution based on alternative direction multiplier method (ADMM), which reduces the overhead of information exchange among coordinated BSs and achieves good approximation performance. Finally, simulation results demonstrate the performance of the proposed AN-aided cross-tier MCBF design and verify the validity of distributed ADMM-based approach

Adaptive clutter filter design for micro-ultrasound color flow imaging of small blood vessels

In micro-ultrasound, which uses imaging frequencies above 20 MHz, obtainingcolor flow images (CFI) of small blood vessels using is not a trivial taskbecause it is more challenging to suppress tissue clutter properly given thestronger blood signal power at high imaging frequencies and the slow bloodvelocity inside the microcirculation. To improve clutter suppression inmicro-ultrasound CFI, this paper presents an adaptive clutter filtering approachthat is based on a two-stage eigen-analysis of slow-time ensemblecharacteristics. The approach first identifies tissue pixels in the imaging viewby examining whether high-frequency contents are absent in the principalslow-time eigen-components for each pixel as computed from single-ensembleeigen-decomposition. It then computes the filtered slow-time ensemble for eachpixel by finding the least-squares projection residual between the pixel'sslow-time ensemble and the clutter eigen-components estimated from amulti-ensemble eigen-decomposition of tissue slow-time ensembles within aspatial window. In this filtering approach, the clutter eigen-components arechosen based on whether their mean frequency lies within a spectral band. Toanalyze the efficacy of the proposed adaptive filter, both in-vitro experimentsand Field II simulations were carried out. For the experiments, raw CFI datawere acquired using a 64-element, 33 MHz linear array prototype (pulse duration:2 cycles, PRF: 1 kHz, transmit focus: 8mm, F-number: 5). Their imaging viewcorresponded to the cross-section of a 0.9mm-diameter tube that was placed ontop of an unsuspended table where ambient vibrations may appear; flow velocity(5, 7, 10, 15 mm/s) within the tube was controlled using a syringe pump. For thesimulations, raw CFI data was computed for both plug and parabolic flowprofiles, and tissue motion was modeled as 0.5 mm/s sinusoidal vibrations. Forall flow velocities tested in our in-vitro study, the proposed adaptive filterimproved the flow detection sensitivity as compared to existing ones. In theslow-flow case (5 mm/s), we observed over 70% increase in flow detectionsensitivity (assuming a 5% false alarm rate). This effectively reduced flashingartifacts in the resulting CFIs and gave a more consistent visualization of theflow tube. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA.,, 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1206-120

Functional MRI of the spinal cord at low field

A second contrast mechanism SEEP was reported to co-exist with BOLD during fMRI activation. The mechanism was based on the task-induced signal change of extravascular water protons and was primarily shown in the spinal cord at high field. Recently, a preliminary study was reported at 0.2T showing SEEP contrast in the brain while the BOLD effect was negligible. The present study is to investigate the presence of SEEP in the spinal cord at 0.2T using proton density-weighted imaging with motor task. Bilateral activations were obtained in the anterior grey horns consistently across C6-C8 levels, which correlated with the neural anatomy.published_or_final_versio

Design of a programmable micro-ultrasound research platform

To foster innovative uses of micro-ultrasound in biomedicine, it is beneficial to develop flexible research-purpose systems that allow researchers to easily reconfigure its system-level operations such as transmit firing sequence and receive processing. In this paper, we present the development of a programmable micro-ultrasound research platform that is capable of realizing various micro-imaging algorithms. The research platform comprises a linear-array-based scanning front-end and a PC-based data processing back-end, which employs a graphical processing unit (GPU) as the processor core. The front-end operations can be configured from the PC via the parallel port and the two blocks are synchronized by an external clock. Acquired data from the front-end is first digitized and relayed to the PC through an data acquisition card (200 MHz, 14-bit). They are then transferred to the GPU (GTX 275) in which the image formation is carried out via multi-thread processing. Results are displayed on-screen in real-time and can be saved to the PC's hard disk for offline analysis. Through a module-based programming approach, this platform can facilitate realization of custom-designed imaging algorithms developed by researchers. In this work, B-mode imaging and adaptive color flow imaging have been implemented as demonstrations of the research platform's programmability. The performance results show that real-time processing frame rates can be achieved for both imaging modes. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1980-198

Multi-Objective Optimization for Spectrum and Energy Efficiency Tradeoff in IRS-Assisted CRNs with NOMA

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the S-procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS

The Synergy of Edge and Central Cloud Computing with Wireless MIMO Backhaul

In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only