In this paper, to minimize the on-grid energy cost in a large-scale green cellular network, we jointly design the optimal base station (BS) ON/OFF operation policy and the on-grid energy purchase policy from a network-level perspective. We consider that the BSs are aggregated as a microgrid with hybrid energy supplies and an associated central energy storage, which can store the harvested renewable energy and the purchased on-grid energy over time. Due to the fluctuations of the on-grid energy prices, the harvested renewable energy, and the network traffic loads over time, as well as the BS coordination to hand over the traffic offloaded from the inactive BSs to the active BSs, it is generally NP-hard to find a network-level optimal adaptation policy that can minimize the on-grid energy cost over a long-term and yet assures the downlink transmission quality at the same time. Aiming at the network-level dynamic system design, we jointly apply stochastic geometry (Geo) for large-scale green cellular network analysis and dynamic programming (DP) for adaptive BS ON/OFF operation design and on-grid energy purchase design, and thus propose a new Geo-DP design approach. By this approach, we obtain the optimal BS ON/OFF policy, which shows that the optimal BSs' active operation probability in each horizon is just sufficient to assure the required downlink transmission quality with time-varying load in the large-scale cellular network. However, due to the curse of dimensionality of the DP, it is of high complexity to obtain the optimal on-grid energy purchase policy. We thus propose a suboptimal on-grid energy purchase policy with low complexity, where the low-price on-grid energy is over purchased in the current horizon only when the current storage level and the future renewable energy level are both low. Simulation results show that the suboptimal on-grid energy purchase can achieve near-optimal performance. We also compare the proposed policy with the existing schemes to show that our proposed policy can more efficiently save the on-grid energy cost over time.
In this letter, we propose multi-antenna based spectrum sensing methods for cognitive radios (CRs) using the generalized likelihood ratio test (GLRT) paradigm. The proposed methods utilize the eigenvalues of the sample covariance matrix of the received signal vector from multiple antennas, taking advantage of the fact that in practice, the primary user signals to be detected will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a non-white spatial spectrum. These methods do not require prior knowledge of the primary user signals, or the channels from the primary users to the CR. By making different assumptions on the availability of the white noise power value at the CR receiver, we derive two algorithms that are shown to outperform the standard energy detector.
Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users' common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a high-quality suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.
In intelligent reflecting surface (IRS) aided wireless communication systems, channel state information (CSI) is crucial to achieve its promising passive beamforming gains. However, CSI errors are inevitable in practice and generally correlated over the IRS reflecting elements due to the limited training with discrete phase shifts, which degrade the data transmission rate and reliability. In this paper, we focus on investigating the effect of CSI errors to the outage performance in an IRS-aided multiuser downlink communication system. Specifically, we aim to jointly optimize the active transmit precoding vectors at the access point (AP) and passive discrete phase shifts at the IRS to minimize the AP's transmit power, subject to the constraints on the maximum CSI-error induced outage probability for the users. First, we consider the single-user case and derive the user's outage probability in terms of the mean signal power (MSP) and variance of the received signal at the user. Since there is a trade-off in tuning these two parameters to minimize the outage probability, we propose to maximize their weighted sum with the optimal weight found by one-dimensional search. Then, for the general multiuser case, since the users' outage probabilities are difficult to obtain in closed-form due to the inter-user interference, we propose a novel constrained stochastic successive convex approximation (CSSCA) algorithm, which replaces the non-convex outage probability constraints with properly designed convex surrogate approximations. Simulation results verify the effectiveness of the proposed robust beamfoming algorithms and show their significant performance improvement over various benchmark schemes.
Cognitive beamforming (CB) is a multiantenna technique for efficient spectrum sharing between primary users (PUs) and secondary users (SUs) in a cognitive radio network. Specifically, a multiantenna SU transmitter applies CB to suppress the interference to the PU receivers as well as enhance the corresponding SU-link performance. In this paper, for a multiple-input-single-output (MISO) SU channel coexisting with a single-input-single-output (SISO) PU channel, we propose a new and practical paradigm for designing CB based on the finite-rate cooperative feedback from the PU receiver to the SU transmitter. Specifically, the PU receiver communicates to the SU transmitter the quantized SU-to-PU channel direction information (CDI) for computing the SU transmit beamformer, and the interference power control (IPC) signal that regulates the SU transmission power according to the tolerable interference margin at the PU receiver. Two CB algorithms based on the PU's cooperative feedback are proposed: one restricts the SU transmit beamformer to be orthogonal to the quantized SU-to-PU channel direction and the other relaxes such a constraint. In addition, cooperative feedforward of the SU CDI from the SU transmitter to the PU receiver is exploited to allow more efficient cooperative feedback from the PU receiver. The outage probabilities of the SU link for different cooperative feedback/feedforward algorithms are analyzed, from which the optimal bit-allocation tradeoff between the CDI and IPC feedback is characterized.
No abstract is provided for this article.
Wireless power transfer (WPT) technology provides a cost-effective solution to achieve a 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 votes for some strong sub-channels, 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. Toward 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. Reciprocally, it also consumes lower transmit power to achieve nearly perpetual network operation.
In this letter, we study the passive beamforming design for optimizing the three-dimensional (3-D) coverage performance in an intelligent reflecting surface (IRS)-assisted communication system. In particular, we maximize the minimum (i.e., max-min) beamforming gain over all locations in the target area by jointly optimizing the passive reflection of all IRS elements. The resulting problem has a non-convex and semi-infinite beamforming gain constraint and a non-convex constant-modulus constraint. Both of them are reformulated to be finite and convex in the lifted matrix space with an additional rank-one constraint, and a difference-of-convex technique is developed to solve the reformulated problem efficiently. Finally, we demonstrate that the proposed passive beamforming solution outperforms the existing methods and can approach the upper bound on the max-min beamforming gain.
Because of the complexity of battlefield environment,there exist many dynamic and uncertain causal relationships between operational task and goal.Currently,the traditional analytical model and combat simulation are not competent for causal modeling and efficient execution in calculating operational task effectiveness.Timed influence nets are introduced.The self loop and strength parameter are used to extend time constraints.So the asynchronous and synchronous relationships between combat actions can be expressed further.A method of calculating operational task effectiveness based on extended timed influence nets is proposed.In a certain battle scenario,this calculation method is proved to be feasible and valid by a landing task.
The low efficiency of far-field wireless power transfer (WPT) limits the fundamental rate-energy (R-E) performance trade-off of the simultaneous wireless information and power transfer (SWIPT) system. To address this challenge, we propose in this letter a new SWIPT system aided by the emerging intelligent reflecting surface (IRS) technology. By leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts, IRS achieve a high passive beamforming gain, which is appealing for drastically enhancing the WPT efficiency and thereby the R-E trade-off of SWIPT systems. We consider an IRS being deployed to assist a multi-antenna access point (AP) to serve multiple information decoding receivers (IDRs) and energy harvesting receivers (EHRs). We aim to maximize the weighted sum-power received by EHRs via jointly optimizing the transmit precoders at the AP and reflect phase shifts at the IRS, subject to the individual signal-to-interference-plus-noise ratio (SINR) constraints for IDRs. Since this problem is non-convex, we propose efficient algorithms to obtain suboptimal solutions for it. In particular, we prove that it is sufficient to send information signals only at the AP to serve both IDRs and EHRs regardless of their channel realizations. Moreover, simulation results show significant performance gains achieved by our proposed designs over benchmark schemes.
Monitoring aggregates on IP traffic data streams is a compelling application for data stream management systems. The need for exploratory IP traffic data analysis naturally leads to posing related aggregation queries on data streams, that differ only in the choice of grouping attributes. In this paper, we address this problem of efficiently computing multiple aggregations over high speed data streams, based on a two-level LFTA/HFTA DSMS architecture, inspired by Gigascope.Our first contribution is the insight that in such a scenario, additionally computing and maintaining fine-granularity aggregation queries (phantoms) at the LFTA has the benefit of supporting shared computation. Our second contribution is an investigation into the problem of identifying beneficial LFTA configurations of phantoms and user-queries. We formulate this problem as a cost optimization problem, which consists of two sub-optimization problems: how to choose phantoms and how to allocate space for them in the LFTA. We formally show the hardness of determining the optimal configuration, and propose cost greedy heuristics for these independent sub-problems based on detailed analyses. Our final contribution is a thorough experimental study, based on real IP traffic data, as well as synthetic data, to demonstrate the effectiveness of our techniques for identifying beneficial configurations.
To enhance the national security, there is a growing need for authorized parties to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario, where a legitimate monitor aims to intercept a suspicious wireless link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. We propose a new approach, namely, proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver in a full-duplex mode so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. By assuming perfect self-interference cancelation (SIC) and global channel state information (CSI) at the legitimate monitor, we characterize the fundamental information-theoretic limits of proactive eavesdropping. We consider both delay-sensitive and delay-tolerant applications for the suspicious communication, under which the legitimate monitor maximizes the eavesdropping non-outage probability (for event-based monitoring) and the relative eavesdropping rate (for content analysis), respectively, by optimizing the jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms other benchmark schemes. Furthermore, by extending to a more practical scenario with residual SI and local CSI, we design an efficient online cognitive jamming scheme inspired by the optimal cognitive jamming with perfect SIC and global CSI.
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. 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.
No abstract is provided for this article.
In this paper, we study the optimal node placement problem in wireless powered communication networks (WPCNs), where the wireless devices (WDs) harvest the radio frequency energy transferred by dedicated energy nodes (ENs) in the downlink, and use the harvested energy to transmit data to information access points (APs) in the uplink. In particular, we are interested in minimizing the deployment cost on ENs and APs, while satisfying the energy harvesting and communication performance requirements of the WDs. Specifically, we first study the optimal placement of ENs given fixed AP locations, where an efficient greedy algorithm is proposed to tackle the non-convexity of the problem. Based on the obtained results, we further propose an alternating optimization method that jointly optimizes the placements of ENs and APs. Simulation results show that the proposed methods can effectively reduce the network deployment cost to guarantee the given performance requirements, which is a key consideration in the future applications of WPCNs.
This paper studies the optimal training design for a multiple-input single-output (MISO) wireless energy transfer (WET) system in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both achieved by properly learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the sub-band that exhibits the largest sum-power over all the antennas at the ET is determined and its index is sent back to the ER. In the second phase, the selected sub-band is further trained for the ET to estimate the multi-antenna channel and implement energy beamforming. We propose to maximize the net energy harvested at the ER, which is the total harvested energy offset by that used for the two-phase channel training. The optimal training design, including the number of sub-bands trained and the energy allocated for each of the two phases, is derived.
