Cloud radio access network (C-RAN) with centralized baseband processing is envisioned as a promising candidate for the next-generation wireless communication network. However, the joint processing gain of C-RAN is fundamentally constrained by the finite-capacity fronthaul links between the central unit (CU) where joint processing is implemented and distributed access points known as remote radio heads (RRHs). In this paper, we consider the downlink communication in a C-RAN with multi-antenna RRHs and single-antenna users, and investigate the joint RRH beamforming and user-RRH association problem to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users subject to each RRH's individual fronthaul capacity constraint. The formulated problem is in general NP-hard due to the fronthaul capacity constraints and thus is difficult to be solved optimally. In this paper, we propose a new iterative method for this problem which decouples the design of beamforming and user association, where the number of users served by each RRH is iteratively reduced until the obtained beamforming and user association solution satisfies the fronthaul capacity constraints of all RRHs. A monotonic convergence is proved for the proposed algorithm, and it is shown by simulation that the algorithm achieves significant performance improvement over other heuristic solutions.
To achieve the more promising passive beamforming gains in the double-intelligent reflecting surface (IRS) assisted system over the conventional single-IRS system, channel estimation is practically indispensable but also a more challenging problem to tackle, due to the presence of not only the single- but also double-reflection links that are intricately coupled. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS assisted uplink multiple-input multiple-output (MIMO) communication system to resolve the cascaded channel state information (CSI) of both its single- and double-reflection links. First, for the single-user case, the higher-dimensional double-reflection channel is efficiently estimated at the multi-antenna base station (BS) with low training overhead by exploiting the fact that its cascaded channel coefficients are scaled versions of those of a lower-dimensional single-reflection channel. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel estimated as in the single-user case, the other users' cascaded channels are scaled versions of it and thus can be estimated with reduced training overhead. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to the benchmark scheme.
This letter studies a radio-frequency multi-user wireless power transfer (WPT) system, where an energy transmitter (ET) with a large number of antennas delivers energy wirelessly to multiple distributed energy receivers (ERs). We investigate a low-complexity WPT scheme based on the retrodirective beamforming technique, where all ERs send a common beacon signal simultaneously to the ET in the uplink and the ET simply conjugates and amplifies its received sum-signal and transmits to all ERs in the downlink for WPT. We show that such a low-complexity scheme achieves the massive multiple-input multiple-output energy beamforming gain. However, a "doubly near-far" issue exists due to the round-trip (uplink beacon and downlink WPT) signal propagation loss where the harvested power of a far ER from the ET can be significantly lower than that of a near ER if the same uplink beacon power is used. To tackle this problem, we propose a distributed uplink beacon power update algorithm, where each ER independently adjusts the beacon power based on its current harvested power in an iterative manner. It is shown that the proposed algorithm converges quickly to a unique fixed-point solution, which helps achieve the desired user fairness with best efforts.
Intelligent reflecting surface (IRS) is a promising technology for achieving high spectrum efficiency in future wireless networks by leveraging massive low-cost reflecting elements with each reflecting the incident signal with a proper phase shift. However, prior works on IRS are mainly based on the optimization of infinite-resolution phase shifters which are practically infeasible due to hardware imperfections. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only finite-resolution phase shifter available at each element is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. We aim to minimize the transmit power at the AP by jointly optimizing the transmit beamforming at the AP and reflect beamforming at the IRS, subject to the signal-to-noise ratio (SNR) constraint and practical discrete phase shift constraints. We first propose a suboptimal but low-complexity algorithm by exploiting the alternating optimization technique. Then, we reveal that as in the case with continuous phase shifts, the IRS with discrete phase shifts also achieves the squared power gain for asymptotically large number of reflecting elements, despite suffering a performance loss that depends only on the resolution of phase shifters.
In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1 - ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the ESD efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. The optimal save-ratio that minimizes outage probability is derived, from which some useful design guidelines are drawn. Numerical results are provided to validate our proposed study.
In this paper, we study a new intelligent refracting surface (IRS)-assisted high-mobility communication with the IRS deployed in a high-speed moving vehicle to assist its passenger's communication with a static base station (BS) on the roadside. The vehicle's high Doppler frequency results in a fast fading channel between the BS and the passenger/user, which renders channel estimation for the IRS with a large number of refracting elements a more challenging task as compared to the conventional case with low-mobility users only. In order to mitigate the Doppler effect and reap the full IRS passive beamforming gain with low training overhead, we propose a new and efficient transmission protocol to execute channel estimation and IRS refraction design for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed, we first estimate the cascaded BS-IRS-user channel with the Doppler effect compensated. Then, we estimate the instantaneous BS-user fast fading channel (without IRS refraction) and tune the IRS refraction over time accordingly to align the cascaded channel with the BS-user direct channel, thus maximizing the IRS's passive beamforming gain as well as converting their combined channel from fast to slow fading. Simulation results show the effectiveness of the proposed channel estimation scheme and passive beamforming design as compared to various benchmark schemes.
Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices can be optimized for different subcarriers while only one common set of IRS reflection coefficients can be designed to cater to all subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.
In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is assumed to be a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time {\rho} (dubbed the save-ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1 - {\rho} used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the energy storage efficiency, {\eta}. We address the practical case of the secondary ESD being a battery with {\eta} < 1, and the main ESD being a super-capacitor with {\eta} = 1. The optimal save-ratio that minimizes outage probability is derived, from which some useful design guidelines are drawn. In addition, we compare the outage performance of random power supply to that of constant power supply over the Rayleigh fading channel. The diversity order with random power is shown to be the same as that of constant power, but the performance gap can be large. Furthermore, we extend the proposed ST protocol to wireless networks with multiple transmitters. It is shown that the system-level outage performance is critically dependent on the relationship between the number of transmitters and the optimal save-ratio for single-channel outage minimization. Numerical results are provided to validate our proposed study.
In this paper, we propose a new distributed frequency control scheme for electric vehicles (EVs) to help restore the power grid frequency upon a contingency of supply-demand imbalance. Under our scheme, each EV independently monitors the grid frequency at discrete times and responds by switching among its charging, idle, and discharging operational modes according to a simple threshold-based switching algorithm. To recover the grid frequency smoothly and prevent an undesired frequency overshoot/undershoot due to simultaneous response of EVs, we design the inter-response times of any EV to follow an exponentially distributed random variable with a certain mean value at each operational mode. To draw insights into the performance of our scheme, we characterize its impacts on the grid frequency in various aspects, including the mean and variance of the resulting grid frequency over time, the mean frequency recovery time, the average number of EV switching their modes, and the probability of frequency overshoot/undershoot. Accordingly, we formulate an optimization problem for the grid operator to minimize the expected cost of implementing our frequency control scheme by designing EVs response rates subject to their requested incentive prices and the given grid performance guarantees. Finally, we validate our analysis via simulations on the IEEE 9-Bus test system and the Ireland power system, where it is observed that our frequency control scheme can be used as a reliable and cost-efficient alternative for the conventional primary reserve service.
To enhance the national security, there is a growing need for government agencies 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 communication 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. In practice, such legitimate eavesdropping is particularly challenging, since the legitimate monitor may be far away from the suspicious transmitter and cannot eavesdrop efficiently. To overcome this issue, we propose a new approach, namely proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. In particular, we consider delay-sensitive and delay-tolerant applications for the suspicious data communications, 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 its 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 the conventional passive eavesdropping without jamming and the proactive eavesdropping with constant-power jamming.
In distributed, service-oriented environments, performance problem localization is required to provide self-healing capabilities and deliver the desired quality of service (QoS). This paper presents an automated approach to identifying system elements causing performance problems. Applying probabilistic inference to collected response time and elapsed time data, the approach 1) infers elapsed time for services where data is missing, 2) estimates the response time degradation caused by different services using the duration, abnormality and response time correlation of their elapsed times, and 3) identifies the services that are the most important causes of slow response time and yield the most benefit if recovered. The approach has been used to localize a performance problem on the test bed of a real-world service-oriented Grid. Evaluation using simulations shows that the approach consistently achieves better accuracy than traditional techniques in various service-oriented settings.
The use of unmanned aerial vehicles (UAVs) as aerial communication platforms is of high practical value to future wireless systems such as 5G, especially for swift and on-demand deployment in temporary events and emergency situations. Compared to the static terrestrial base stations (BSs) in cellular networks, UAV-mounted aerial BSs possess stronger line-of-sight links with the ground users due to their high altitude as well as high and flexible mobility in 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 onboard energy, but require high propulsion power to stay airborne and support high mobility. Motivated by the above new considerations, this article aims to revisit some fundamental trade-offs in UAV-enabled communication and trajectory design. Specifically, it is shown that the 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 existing trade-offs in terrestrial communication. The main design challenges and promising directions for future research are also discussed.
With the rapid development of artificial intelligence technology (AIT) and communication technology, China's traditional power grid (TPG) is developing in the direction of smart grid (SG). The application of power Internet of things based on Internet of things in SG is the proof of the development of information technology to a certain level. The main function of China's power grid is to monitor the daily operation of power consumption, collect and integrate the power consumption data of each household. However, with the rapid development of the current society, a large number of power consumption data makes the TPG start to operate under load, so the SG comes into being. After the reasonable operation of data collection and integration, how to screen bad data has become a new challenge for power Internet of things. The purpose of this paper is to study the bad data screening algorithm of power Internet of things based on AIT. This paper mainly expounds the types of power Internet of things data classification processing and analysis, and understands its batch processing and analysis, near real-time analysis and stream processing analysis. At the same time, this paper describes the process of data processing screening, analyzes the data screening process and the problems that the algorithm will encounter, makes algorithm analysis for the data screening of power Internet of things, and proves the reliability of the bad data screening algorithm. At the same time, the processing time and efficiency of TPG and SG are analyzed. It is proved that under the same workload, the efficiency of power Internet of things data algorithm based on AIT is much higher than that of TPG. The experimental results show that the working time of the power Internet of things is less than that of the TPG, but the working efficiency is far better than that of the TPG. When processing 500000 pieces of data, the working time of the TPG is 574 seconds, and the working efficiency is 1.52, while the working time of the power Internet of things is 162 seconds, and the working efficiency is 1.12.
This paper studies the power control schemes for MIMO-OFDM systems assuming that perfect channel state information is available to both transmitter and receiver sides. Suppose M is the minimum of the transmit/receive antenna numbers, N is the number of subcarriers, we propose a sub-channel grouping technique which groups the MN space-frequency sub-channels into M grouped channels. Three power control schemes are then proposed to allocate the transmission powers over the grouped channels. The proposed schemes perform water-filling over M dimensions rather than MN dimensions, thus they have reduced computation and implementation complexity. Computer simulations are given to evaluate and to compare the capacities achieved by different schemes.
