As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information Manuscript
Abstract-Wireless energy transfer (WET) has attracted significant attention recently for delivering energy to electrical devices without the need of wires or power cables. In particular, the radio frequency (RF) signal enabled far-field WET is appealing to power energy-constrained wireless networks in a broadcast manner. To overcome the significant path loss over wireless channels, multi-antenna or multiple-input multiple-output (MIMO) techniques have been proposed to enhance both the transmission efficiency and range for RF-based WET. However, in order to reap the large energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging for WET systems, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels at the same time without reducing the analytic convergence speed. Furthermore, through extensive simulations, we show that the proposed algorithm outperforms existing one-bit feedback based channel learning schemes in terms of both convergence speed and energy transfer efficiency, especially when the number of ERs becomes large.Index Terms-Wireless energy transfer (WET), multiple-input multiple-output (MIMO), energy beamforming, channel learning, one-bit feedback, analytic center cutting plane method (ACCPM).
Characterizing the fundamental energy efficiency (EE) limits of MIMO broadcast channels (BC) is significant for the development of green wireless communications. We address the EE optimization problem for MIMO-BC in this paper and consider a practical power model, i.e., taking into account a transmit independent power which is related to the number of active transmit antennas. Under this setup, we propose a new optimization approach, in which the transmit covariance is optimized under fixed active transmit antenna sets, and then active transmit antenna selection (ATAS) is utilized. During the transmit covariance optimization, we propose a globally optimal energy efficient iterative water-filling scheme through solving a series of concave fractional programs based on the block-coordinate ascent algorithm. After that, ATAS is employed to determine the active transmit antenna set.Since activating more transmit antennas can achieve higher sum-rate but at the cost of larger transmit independent power consumption, there exists a tradeoff between the sum-rate gain and the power consumption. Here ATAS can exploit the best tradeoff and thus further improve the EE. Optimal exhaustive search and low-complexity norm based ATAS schemes are developed. Through simulations, we discuss the effect of different parameters on the EE of the MIMO-BC. Index TermsEnergy efficiency, spectral efficiency, MIMO broadcast channels, energy efficient iterative water-filling, antenna selection.
In this paper, we pursue a unified study on smart grid and coordinated multi-point (CoMP) enabled wireless communication by investigating a new joint communication and energy cooperation approach. We consider a practical CoMP system with clustered multiple-antenna base stations (BSs) cooperatively communicating with multiple single-antenna mobile terminals (MTs), where each BS is equipped with local renewable energy generators to supply power and also a smart meter to enable two-way energy flow with the grid. We propose a new energy cooperation paradigm, where a group of BSs dynamically share their renewable energy for more efficient operation via locally injecting/drawing power to/from an aggregator with a zero effective sum-energy exchanged. Under this new energy cooperation model, we consider the downlink transmission in one CoMP cluster with cooperative zero-forcing (ZF) based precoding at the BSs. We maximize the weighted sum-rate for all MTs by jointly optimizing the transmit power allocations at cooperative BSs and their exchanged energy amounts subject to a new type of power constraints featuring energy cooperation among BSs with practical loss ratios. Our new setup with BSs' energy cooperation generalizes the conventional CoMP transmit optimization under BSs' sum-power or individual-power constraints. It is shown that with energy cooperation, the optimal throughput is achieved when all BSs transmit with all of their available power, which is different from the conventional CoMP schemes without energy cooperation where BSs' individual power constraints may not be all tight at the same time. This result implies that some harvested energy may be wasted without any use in the conventional setup due to the lack of energy sharing among BSs, whereas the total energy harvested at all BSs is efficiently utilized for throughput maximization with the proposed energy cooperation, thus leading to a new energy cooperation gain. Finally, we validate our results by simulations under various practical setups, and show that the proposed joint communication and energy cooperation scheme substantially improves the downlink throughput of CoMP systems powered by smart grid and renewable energy, as compared to other suboptimal designs without communication and/or energy cooperation.
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