Wirelessly powered backscatter communication (WPBC) has been identified as a promising technology for low-power communication systems, which can reap the benefits of energy beamforming to improve energy transfer efficiency. Existing studies on energy beamforming fail to simultaneously take energy supply and information transfer in WPBC into account. This paper takes the first step to fill this gap, by considering the trade-off between the energy harvesting rate and achievable rate using estimated backscatter channel state information (BS-CSI). To ensure reliable communication and user fairness, we formulate the energy beamforming design as a max-min optimization problem by maximizing the minimum achievable rate for all tags subject to the energy constraint. We derive the closed-form expression of the energy harvesting rate, as well as the lower bound of the ergodic achievable rate. Our numerical results indicate that our scheme can significantly outperform state-of-the-art energy beamforming schemes. Additionally, the proposed scheme achieves performance comparable to that obtained via beamforming with perfect CSI.
Significant progress has recently been made on the energy beamforming in wireless powered communication @cite_5 @cite_1 @cite_2 @cite_9 . To harness the benefits of energy beamforming, there have been many efforts made to enable energy beamforming in WPBC. @cite_0 focus on optimizing the transmit beamforming to maximize the sum rate of a cooperative WPBC system. @cite_8 investigate energy beamforming in a relay WPBC system. Both these studies assume that F-CSI can be known to achieve energy beamforming. However, the closed-loop propagation and power-limited tags make it hard to obtain F-CSI. Instead, @cite_10 performs energy beamforming via the estimated BS-CSI to improve WET efficiency. Departure from these studies, our work considers the beamforming design for both energy supply and data transfer to ensure communication performance.
@cite_8 @cite_9 @cite_1 @cite_0 @cite_2 @cite_5 @cite_10
2791237814 2897693927 2963423274 2783633736 2888460575 2584574674 2964056649
The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS’ power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance. This paper studies the beneficial combination of wireless power transfer and millimeter-wave (mmWave) communications in wireless sensor networks. In particular, an mmWave wireless powered sensor network is considered, where the access point (AP) employs beamforming techniques to transfer energy to the sensors of a selected sector of the cell. The served sensors harvest and store energy from the received signal, and use it to power their uplink transmissions. We consider a random energy beamforming scheme but also propose several intelligent schemes that steer the beam to specific areas of the cell by considering the sensors’ locations. This setup is investigated from a large-scale point-of-view where spatial randomness is considered with the aid of Poisson point processes. The performance of the network is described in terms of the energy outage probability and the beam outage probability. We show that, depending on the scenario, each of the considered energy beamforming schemes can provide significant gains to the network’s performance. Finally, we study an event monitoring application of our theoretical framework, where the active sensors observe a random event in the network and the AP attempts to estimate it based on the received information; this scenario is evaluated in terms of the estimation’s mean squared error. In this paper, the joint beamforming design for max-min fair simultaneous wireless information and power transfer (SWIPT) is investigated in a green cloud radio access network (Cloud-RAN) with millimeter wave (mmWave) wireless fronthaul. To achieve a balanced user experience for separately located data receivers (DRs) and energy receivers (ERs) in the network, joint transmit beamforming vectors will be optimized to maximize the minimum data rate among all the DRs, while satisfying each ER with sufficient RF energy at the same time. Then, a two-step iterative algorithm is proposed to solve the original non- convex optimization problem with the fronthaul capacity constraint in an @math -norm form. Specifically, the @math - norm constraint can be approximated by the reweighted @math -norm, from which the optimal max-min data rate and the corresponding joint beamforming vector can be derived via semidefinite relaxation (SDR) and bi-section search. Finally, extensive numerical simulations are performed to verify the superiority of the proposed joint beamforming design to other separate beamforming strategies. Ambient backscatter communication (AmBC) enables a tag to modulate its information bits over ambient RF carriers by intentionally changing its reflection coefficient, thus has emerged as a promising technique to achieve green communications for future Internet-of-Things. In this paper, we model a cooperative AmBC system from a spectrum- sharing perspective, where a cooperative receiver (C-RX) decodes the information from both a multi-antenna primary transmitter (PT) and a single-antenna secondary transmitter (i.e., tag). We consider two scenarios: first, the tag-symbol period equals the PT-symbol period; second, the tag-symbol period is an integer multiple of the PT-symbol period. For each scenario, we analyze the data rate via successive- interference-cancellation (SIC) based decoding, and formulate a problem to maximize the sum rate by optimizing the beamforming vector at the PT. The problems are transformed into semi-definite programming (SDP), and solved by using the technique of semi-definite relaxation (SDR). Furthermore, a novel transmit beamforming structure is proposed to reduce the computational complexity of beamforming optimization. Numerical results show that the cooperative AmBC system can achieve a higher sum rate than a conventional point-to-point system without a backscatter tag. Massive MIMO is attractive for wireless information and energy transfer due to its ability to focus energy toward desired spatial locations. In this paper, the overall power transfer efficiency (PTE) and the energy efficiency (EE) of a wirelessly powered massive MIMO system are investigated, where a multi-antenna base-station (BS) uses wireless energy transfer to charge single-antenna energy harvesting users on the downlink. The users may exploit the harvested energy to transmit information to the BS on the uplink. The overall system performance is analyzed while accounting for the nonlinear nature of practical energy harvesters. First, for wireless energy transfer, the PTE is characterized using a scalable model for the BS circuit power consumption. The PTE-optimal values for the number of BS antennas and users are derived. Then, for wireless energy and information transfer, the EE performance is characterized. The EE-optimal BS transmit power is derived in terms of the key system parameters, such as the number of BS antennas and the number of users. As the number of antennas becomes large, increasing the transmit power improves the EE for moderate to large number of antennas. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime. This paper analyzes the performance of information and energy beamforming in multiple-input multiple- output (MIMO) wireless communications systems, where a self-powered multi-antenna hybrid access point (AP) coordinates wireless information and power transfer (WIPT) with an energy-constrained multi-antenna user terminal (UT). The wirelessly powered UT scavenge energy from the hybrid AP radio-frequency (RF) signal in the downlink (DL) using the harvest-then-transmit protocol, then uses the harvested energy to send its information to the hybrid AP in the uplink (UL). To maximize the overall signal-to-noise ratio (SNR) as well as the harvested energy so as to mitigate the severe effects of fading and enable long-distance wireless power transfer, information and energy beamforming is investigated by steering the transmitted information and energy signals along the strongest eigenmode. To this end, exact and lower-bound expressions for the outage probability and ergodic capacity are presented in closed-form, through which the throughput of the delay- constrained and delay-tolerant transmission modes are analyzed, respectively. Numerical results sustained by Monte Carlo simulations show the exactness and tightness of the proposed analytical expressions. The impact of various parameters such as energy harvesting time, hybrid AP transmit power and the number of antennas on the system throughput is also considered. We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10 , compared to that obtained using the perfect F-CSI.