Wireless power transfer (WPT) technology provides a cost-effective solution to achieve sustainable energy supply in wireless networks, where WPT-enabled energy nodes (ENs) can charge wireless devices (WDs) remotely without interruption to the use. However, in a heterogeneous WPT network with distributed ENs and WDs, some WDs may quickly deplete their batteries due to the lack of timely wireless power supply by the ENs, thus resulting in short network operating lifetime. In this paper, we exploit frequency diversity in a broadband WPT network and study the distributed charging control by ENs to maximize network lifetime. In particular, we propose a practical voting-based distributed charging control framework where each WD simply estimates the broadband channel, casts its vote(s) for some strong sub-channel(s) and sends to the ENs along with its battery state information, based on which the ENs independently allocate their transmit power over the sub-channels without the need of centralized control. Under this framework, we aim to design lifetime-maximizing power allocation and efficient voting-based feedback methods. Towards this end, we first derive the general expression of the expected lifetime of a WPT network and draw the general design principles for lifetime-maximizing charging control. Based on the analysis, we then propose a distributed charging control protocol with voting-based feedback, where the power allocated to sub-channels at each EN is a function of the weighted sum vote received from all WDs. Besides, the number of votes cast by a WD and the weight of each vote are related to its current battery state. Simulation results show that the proposed distributed charging control protocol could significantly increase the network lifetime under stringent transmit power constraint in a broadband WPT network.
This paper studies the information-theoretic limits for the fading multiple-input single-output broadcast channel (MISO-BC) for support of data traffic with heterogeneous transmission delay requirements. First, this paper derives the optimal dynamic resource allocation algorithm for simultaneous transmission of both delay-tolerant "packet" data and delay-sensitive "circuit" data by applying a two-layer Lagrange-duality method. Secondly, this paper investigates the fundamental throughput-delay tradeoff for the fading MISO-BC by characterizing the network throughput loss under minimal versus completely relaxed transmission delay constraint, termed the delay penalty, associated with dynamic resource allocation.
This paper studies spatial capacity in a stochastic wireless ad hoc network, where multi-stage probing and data transmission are sequentially performed. We propose a novel signal-to-interference-ratio (SIR) threshold based scheduling scheme, where by starting with the first probing, each transmitter iteratively decides to further probe or stay idle, depending on whether the estimated SIR in the proceeding probing is larger or smaller than a predefined threshold. Although one can assume that the transmitters are initially deployed according to a homogeneous Poisson point process (PPP), the SIR based scheduling makes the PPP no longer applicable to model the locations of retained transmitters in the subsequent probing and data transmission phases, due to the interference induced coupling in their decisions. We first focus on single-stage probing and find that when the SIR threshold is set sufficiently small to assure an acceptable interference level in the network, the proposed scheme can greatly outperform the non-scheduling reference scheme in terms of spatial capacity. We clearly characterize the spatial capacity and obtain exact/approximate closed-form expressions, by proposing a new approximate approach to deal with the correlated SIR distributions over non-Poisson point processes. Then we successfully extend to multi-stage probing by properly designing the multiple SIR thresholds to assure gradual improvement of the spatial capacity. Furthermore, we analyze the impact of multi-stage probing overhead and present a probing-capacity tradeoff in scheduling design. Finally, extensive numerical results are presented to demonstrate the performance of the proposed scheduling as compared to existing schemes.
The wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study, we formulate the wireless power provision problem as a public good provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their private information and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge due to the non-strict concavity of the agents' payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that our design is most beneficial when there are more EUs with more homogeneous channel gains.
In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing techniques to maximize the energy transfer efficiency. Then, we discuss an interesting paradigm named simultaneous wireless information and power transfer (SWIPT), where energy and information are jointly transmitted using the same radio waveform. At last, we review the recent progress in wireless powered communication networks (WPCN), where wireless devices communicate using the power harvested by means of WET. Extensions and future directions are also discussed in each of these areas.
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This paper studies the design of a decentralized multiuser MIMO system for spectrum sharing over a fixed bandwidth, where the coexisting users independently update their transmit covariance matrices for individual rate maximization via an iterative manner. This design problem was usually investigated in the literature by assuming that each user treats the co-channel interference from all the other users as additional noise at its receiver, i.e., the conventional single-user decoding (SUD) for the MIMO channel is applied. In this paper, we propose a new decoding method for the decentralized multiuser MIMO system, whereby each user opportunistically cancels the co-channel interference from some or all of the other users via applying multiuser detection techniques, thus named opportunistic multiuser detection (OMD). This paper studies the optimal transmit covariance matrix for each user to iteratively maximize transmit rate with the proposed OMD, and shows the resultant capacity gains over the conventional SUD.
This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. By assuming Gaussian input, the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called "interference temperature (IT)" constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.
This paper studies the wireless <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-way</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relay</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel</i> (TWRC), where two source nodes, S1 and S2, exchange information through an assisting relay node, R. It is assumed that R receives the sum signal from S1 and S2 in one time-slot, and then amplifies and forwards the received signal to both S1 and S2 in the next time-slot. By applying the principle of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">analogue</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coding</i> (ANC), each of S1 and S2 cancels the so-called "self-interference" in the received signal from R and then decodes the desired message. Assuming that S1 and S2 are each equipped with a single antenna and R with multi-antennas, this paper analyzes the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capacity</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">region</i> of an ANC-based TWRC with linear processing (beamforming) at R. The capacity region contains all the achievable bidirectional rate-pairs of S1 and S2 under the given transmit power constraints at S1, S2, and R. We present the optimal relay beamforming structure as well as an efficient algorithm to compute the optimal beamforming matrix based on convex optimization techniques.
Intelligent reflecting/refracting surface (IRS) is envisioned as a promising technology to reconfigure wireless propagation environment for enhancing the communication performance, by smartly controlling the signal reflection/refraction with a large number of tunable passive elements. In particular, the application of IRS in high-mobility scenarios can convert wireless channels from fast fading to slow fading, thus achieving more reliable communications. In this paper, we first provide an overview of the new applications and opportunities of IRS in high-mobility communications. Next, we present two practical strategies for deploying IRS to aid high-mobility communications, namely, roadside IRS versus vehicle-side IRS, and compare their different channel characteristics, handover requirements, and deployment costs. Then, the main issues in designing IRS-aided high-mobility communications, including node discovery, mode switching, beam alignment/tracking, handover, and multiuser scheduling are discussed for both IRS deployment strategies. Moreover, numerical results are presented to demonstrate the potential performance gains of IRSs in vehicular communications. Finally, new research directions are pointed out for future work.
In this study, a novel data‐driven based dynamic pricing framework is proposed for sharing rooftop photovoltaic (PV) energy in a single apartment building. In this framework, the input includes the load data, electricity price data, and PV power data and the output includes the pricing strategy for local PV generations. Specifically, the building energy management system operator is responsible for setting internal uniform prices of their own rooftop PV productions to facilitate the local PV energy sharing with apartment building users, aiming to maximise the economic profits. To protect the privacy of apartment building users and meanwhile improve the computational efficiency, a neural network is designed for simulating their demand response (DR) behaviours. Besides, the uncertain rooftop PV generations can be duly addressed by using a well‐trained long short‐term memory network, which can capture the future trends of rooftop PV generations in a rolling‐horizon manner. With the information on PV predictions and DR results, a model‐free reinforcement learning method is developed for finding the near‐optimal dynamic pricing strategy. The simulation results verify the effectiveness of the proposed framework in making dynamic pricing strategies with partial or uncertain information.
In this paper, we study the waveform design problem for a single-input single-output (SISO) radio-frequency (RF) wireless power transfer (WPT) system in frequency-selective channels. First, based on the actual non-linear current-voltage model of the diode at the energy receiver, we derive a semi-closed-form expression for the deliverable DC voltage in terms of the incident RF signal and hence obtain the average harvested power. Next, by adopting a multisine waveform structure for the transmit signal of the energy transmitter, we jointly design the multisine signal amplitudes and phases overall frequency tones according to the channel state information (CSI) to maximize the deliverable DC voltage or harvested power. Although our formulated problem is non-convex and difficult to solve, we propose two suboptimal solutions to it, based on the frequency-domain maximal ratio transmission (MRT) principle and the sequential convex optimization (SCP) technique, respectively. Using various simulations, the performance gain of our solutions over the existing waveform designs is shown.
Intelligent reflecting surface (IRS) that enables the control of the wireless propagation environment has been looked upon as a promising technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Applying this new model to an IRS-aided wireless system, we formulate a problem to maximize its achievable rate by jointly optimizing the transmit beamforming and the IRS reflect beamforming. The formulated problem is non-convex and difficult to be optimally solved in general, for which we propose a low-complexity suboptimal solution based on the alternating optimization (AO) technique. Simulation results unveil a substantial performance gain achieved by the joint beamforming optimization based on the proposed phase shift model as compared to the conventional ideal model.
Considering the information interaction and policy learning characteristics of bounded rational users in the process of responding to electricity prices or incentive measures, this paper proposes a user demand response analysis model based on an evolutionary game model on social networks. First, considering that real economic people have information interaction and strategy learning and updating in decision-making, and the information interaction is a complex community system, the information interaction relationship between users is described by constructing a social network; secondly, according to the electricity price theory and The power supply and demand balance relationship, the response decision of a single user will affect the interests of other users, the game model is used to describe the decision-making process of group users; finally, considering the bounded rationality of users' processing of the acquired information, based on the evolution of social networks Game model to describe the user's response process to electricity prices and incentives. Simulations analyze the impact of different social network structures and electricity prices or incentives on user responses. The results show that the enhancement of the small world attribute of the user's social network will improve the user's response, and the increase of the price coefficient will reduce the user's willingness to use electricity.
Intelligent reflecting surface (IRS) can be densely deployed in complex environments to create cascaded line-of-sight (LoS) links between base stations (BSs) and users, which significantly enhance the signal coverage. In this paper, we consider the wireless power transfer (WPT) from a multi-antenna BS to multiple energy users (EUs) by exploiting the signal beam routing via multi-IRS reflections. First, we present a baseline beam routing scheme with each IRS serving at most one EU, where the BS transmits wireless power to all EUs simultaneously while the signals to different EUs undergo disjoint sets of multi-IRS reflection paths. Under this setup, we aim to tackle the joint beam routing and resource allocation optimization problem by jointly optimizing the reflection paths for all EUs, the active/passive beamforming at the BS/each involved IRS, as well as the BS's power allocation for different EUs to maximize the minimum received signal power among all EUs. Next, to further improve the WPT performance, we propose two new beam routing schemes, namely dynamic beam routing and subsurface-based beam routing, where each IRS can serve multiple EUs via different time slots and different subsurfaces, respectively. In particular, we prove that dynamic beam routing outperforms subsurface-based beam routing in terms of minimum harvested power among all EUs. In addition, we show that the optimal performance of dynamic beam routing is achieved by assigning all EUs with orthogonal time slots for WPT. A clique-based optimization approach is also proposed to solve the joint beam routing and resource allocation problems for the baseline beam routing and proposed dynamic beam routing schemes. Numerical results are finally presented, which demonstrate the superior performance of the proposed dynamic beam routing scheme to the baseline scheme.
Multilevel Monte Carlo (MLMC) is a flexible and effective variance reduction technique for accelerating reliability assessments of complex power system. Recently, data-driven surrogate models have been proposed as lower-level models in the MLMC framework due to their high correlation and negligible execution time once trained. However, in resource adequacy assessments, pre-labeled datasets are typically unavailable. For large-scale systems, the efficiency gains from surrogate models are often offset by the substantial time required for labeling training data. Therefore, this paper introduces a speed metric that accounts for training time in evaluating MLMC efficiency. Considering the total time budget is limited, a vote-by-committee active learning approach is proposed to reduce the required labeling calls. A case study demonstrates that, within a given computational budget, active learning in combination with MLMC can result in a substantial reduction variance.
