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Intelligent surface (IS) is envisioned as a promising technology for the sixth-generation (6G) wireless networks, which can effectively reconfigure the wireless propagation environment via dynamically controllable signal reflection/transmission. In particular, integrating passive intelligent surface (IS) into the base station (BS) is a novel solution to enhance the wireless network throughput and coverage both cost-effectively and energyefficiently. In this article, we provide an overview of IS-integrated BSs for wireless networks, including their motivations, practical architectures, and main design issues. Moreover, numerical results are presented to compare the performance of different IS-integrated BS architectures as well as the conventional BS without IS. Finally, promising directions are pointed out to stimulate future research on IS-BS/terminal integration in wireless networks.
Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they can be constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of EB waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain practically, especially in a distributed system with ETs at separate locations. In this paper, we study practical and efficient channel training methods to achieve optimal EB in a distributed WET system. We propose two protocols with and without centralized coordination, respectively, where distributed ETs either sequentially or in parallel adapt their transmit phases based on a low-complexity energy feedback from the ER. The energy feedback only depends on the received power level at the ER, where each feedback indicates one particular transmit phase that results in the maximum harvested power over a set of previously used phases. Simulation results show that the two proposed training protocols converge very fast in practical WET systems even with a large number of distributed ETs, while the protocol with sequential ET phase adaptation is also analytically shown to converge to the optimal EB design with perfect CSI by increasing the training time. Numerical results are also provided to evaluate the performance of the proposed distributed EB and training designs as compared to other benchmark schemes.
Recent advances in wireless power transfer (WPT) technology provide a cost-effective solution to charge wireless devices remotely without disruption to the use. In this paper, we propose an efficient wireless charging control method for exploiting the frequency diversity in multiuser broadband wireless networks, to reduce energy outage and keep the system operating in an efficient and sustainable state. In particular, we first analyze the impact of charging control method to the operating lifetime of a WPT-enabled broadband system. Based on the analysis, we then propose a multi-criteria charging control policy that optimizes the transmit power allocation over frequency by jointly considering the channel state information (CSI) and the battery state information (BSI) of wireless devices. For practical implementation, the proposed scheme is realized by a novel limited CSI estimation mechanism embedded with partial BSI, which significantly reduces the energy cost of CSI and BSI feedback. Simulation results show that the proposed method could significantly increase the network lifetime under stringent transmit power constraint. Reciprocally, it also consumes lower transmit power to achieve near-perpetual network operation than other single-criterion based charging control methods.
Simultaneous wireless information and power transfer (SWIPT) is anticipated to have abundant applications in future machine or sensor based wireless networks by providing wireless data and energy access at the same time. In this paper, we study a multiuser multiple-input single-output (MISO) broadcast SWIPT system, where a multi-antenna access point (AP) sends information and energy simultaneously via spatial multiplexing to multiple single-antenna receivers each of which implements information decoding (ID) or energy harvesting (EH). Since EH receivers in practice operate with considerably higher received power than ID receivers, we propose a receiver location based transmission scheduling, where receivers that are close to the AP are scheduled for EH while those more distant from the AP for ID. We aim to maximize the weighted sum-power transferred to all EH receivers subject to a given set of minimum signal-to-interference-and-noise ratio (SINR) constraints at different ID receivers. In particular, we consider two types of ID receivers (referred to as Type I and Type II, respectively) without or with the capability of cancelling the interference from (a priori known) energy signals. For each type of ID receivers, we formulate the joint information and energy transmit beamforming design as a non-convex quadratically constrained quadratic program (QCQP). First, we obtain the globally optimal solutions for our formulated QCQPs by applying an optimization technique so-called semidefinite relaxation (SDR). It is shown via SDR that no dedicated energy beam is needed for Type I ID receivers to achieve the optimal solution; while for Type II ID receivers, employing no more than one energy beam is optimal. Next, we establish a new form of the celebrated uplink-downlink duality to develop alternative algorithms to obtain the same optimal solutions as SDR.
This paper studies the capacities of block-fading (BF) additive white Gaussian noise (AWGN) channels with multiple transmit and receive antennas, under the assumption that perfect channel state information is available at both the transmitter (CSIT) and the receiver (CSIR). We study both ergodic capacity under no transmission delay constraint and delay-limited capacity for delay-constrained case, and consider the tradeoff between capacity and complexity. Optimal power allocation schemes to achieve ergodic and delay-limited capacities are known as variants of standard water-filling algorithms, which usually requires large computational complexity at the transmitter side. In this paper, we propose a new sub-optimal power allocation scheme, so-called multi-target channel inversion (MT-CI), which can be easily implemented at the transmitter and still asymptotically achieve the performance of optimal schemes. Furthermore, MT-CI transforms the original fading channel into parallel AWGN channels and hence greatly reduces the receiver complexity. In addition, the proposed scheme is shown to be also suitable for simultaneous transmission of multimedia services which may require different levels of quality of service (QOS).
In view of the lack of more practical automatic allocation tools for flight interval allocation, this paper proposes a concept of aircraft operation diagram, provides the drawing method of aircraft operation diagram, abstracting the operation time and space position of aircraft, and representing the operation state and future trend of each aircraft on the same diagram. A flight interval increment algorithm is proposed in combination with the operation diagram to evaluate whether the current operation trend meets the requirements of operation safety, realize automatic monitoring and automatic optimization of flight interval deployment, and provide a feasible and practical method for future control monitoring.
In this letter, we consider an intelligent reflecting surface (IRS)-assisted multiuser communication system, where an IRS is deployed to provide virtual line-of-sight (LoS) links between an access point (AP) and multiple users. We consider the practical codebook-based IRS passive beamforming and study efficient design for IRS reflect beam training, which is challenging due to the large number of IRS reflecting elements. In contrast to the conventional single-beam training, we propose a new multi-beam training method by dividing the IRS reflecting elements into multiple sub-arrays and designing their simultaneous multi-beam steering over time. By simply comparing the received signal power over time, each user can detect its optimal IRS beam direction with a high probability, even without searching over all possible beam directions as the single-beam training. Simulation results show that our proposed multi-beam training significantly reduces the training time of conventional single-beam training and yet achieves comparable IRS passive beamforming performance for data transmission.
This letter studies a wireless system consisting of distributed ground terminals (GTs) communicating with an unmanned aerial vehicle (UAV) that serves as a mobile base station (BS). The UAV flies cyclically above the GTs at a fixed altitude, which results in a cyclical pattern of the strength of the UAV-GT channels. To exploit such periodic channel variations, we propose a new cyclical multiple access (CMA) scheme to schedule the communications between the UAV and GTs in a cyclical time-division manner based on the flying UAV's position. The time allocations to different GTs are optimized to maximize their minimum throughput. It is revealed that there is a fundamental tradeoff between throughput and access delay in the proposed CMA. Simulation results show significant throughput gains over the case of a static UAV BS in delay-tolerant applications.
The use of unmanned aerial vehicles (UAVs) as aerial communication platforms is of high practical value for future wireless systems such as 5G, especially for swift and on-demand deployment in temporary events and emergency situations. Compared to traditional terrestrial base stations (BSs) in cellular network, UAV-mounted aerial BSs possess stronger line-of-sight (LoS) links with the ground users due to their high altitude as well as high and flexible mobility in three-dimensional (3D) space, which can be exploited to enhance the communication performance. On the other hand, unlike terrestrial BSs that have reliable power supply, aerial BSs in practice have limited on-board energy, but require significant propulsion energy to stay airborne and support high mobility. Motivated by the above new considerations, this article aims to revisit some fundamental tradeoffs in UAV-enabled communication and trajectory design. Specifically, it is shown that communication throughput, delay, and (propulsion) energy consumption can be traded off among each other by adopting different UAV trajectory designs, which sheds new light on their traditional tradeoffs in terrestrial communication. Promising directions for future research are also discussed.
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This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.