1,474 publications from this institution
An edge labeling of a connected graph $G = (V, E)$ is said to be local antimagic if it is a bijection $f\colon E \to\{1,\ldots ,|E|\}$ such that for any pair of adjacent vertices $x$ and $y$, $f^+(x)\not= f^+(y)$, where the induced vertex label $f^+(x)= \sum f(e)$, with $e$ ranging over all the edges incident to $x$. The local antimagic chromatic number of $G$, denoted by $\chi_{la}(G)$, is the minimum number of distinct induced vertex labels over all local antimagic labelings of $G$. In this paper, we characterize $s$-bridge graphs with local antimagic chromatic number 2.
Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. The traffic load in cellular networks exhibits significant fluctuations in both space and time, which can be exploited, through cell range adaptation, for energy saving. In this paper, we design short- and long-term BS power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC (including BS on-off control) is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. By identifying the fact that energy saving at BS essentially comes from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS on-off power control, we propose low-complexity suboptimal schemes with various combinations of the two ESMs to investigate their impacts on system energy consumption. It is shown that when the network throughput is low, BS on-off power control is the most effective ESM, while when the network throughput is higher, range adaptation becomes more effective.
Intelligent reflecting surface (IRS) has emerged as a promising technology to enhance the wireless communication network coverage and capacity by dynamically controlling the radio signal propagation environment. In contrast to the existing works that considered active or passive IRS only, we propose in this paper a new hybrid active-passive IRS architecture that consists of both active and passive reflecting elements, thus achieving their combined advantages flexibly. Under a practical channel setup with Rician fading where only the statistical channel state information (CSI) is available, we study the hybrid IRS design in a multi-user communication system. Specifically, we formulate an optimization problem to maximize the achievable ergodic capacity of the worst-case user by designing the hybrid IRS beamforming and active/passive elements allocation based on the statistical CSI, subject to various practical constraints on the active-element amplification factor and amplification power consumption, as well as the total active and passive elements deployment budget. To solve this challenging problem, we first approximate the ergodic capacity in a simpler form and then propose an efficient algorithm to solve the problem optimally. Moreover, we show that for the special case with all channels to be line-of-sight (LoS), only active elements need to be deployed when the total deployment budget is sufficiently small, while both active and passive elements should be deployed with a decreasing number ratio when the budget increases and exceeds a certain threshold. Finally, numerical results are presented which demonstrate the performance gains of the proposed hybrid IRS architecture and its optimal design over the conventional schemes with active/passive IRS only under various practical system setups.
In this paper, we formulate and solve a weighted-sum transmitter and receiver energy minimization (WSTREMin) problem in the downlink of an orthogonal frequency division multiplexing (OFDM) based multiuser wireless system. The proposed approach offers the flexibility of assigning different levels of importance to base station (BS) and mobile terminal (MT) power consumption, with the BS being connected to the grid and the MT relying on batteries. To obtain insights into the problem, we first consider two extreme cases separately, i.e., weighted-sum receiver-side energy minimization (WSREMin) for MTs and transmitter-side energy minimization (TEMin) for the BS. It is shown that Dynamic TDMA (D-TDMA), where MTs are scheduled for single-user OFDM transmissions over orthogonal time slots, is the optimal transmission strategy for WSREMin at MTs, while OFDMA is optimal for TEMin at the BS. As a hybrid of the two extreme cases, we further propose a new multiple access scheme, i.e., Time-Slotted OFDMA (TS-OFDMA) scheme, in which MTs are grouped into orthogonal time slots with OFDMA applied to users assigned within the same slot. TS-OFDMA can be shown to include both D-TDMA and OFDMA as special cases. Numerical results confirm that the proposed schemes enable a flexible range of energy consumption tradeoffs between the BS and MTs.
Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). Although the MRC enabled WPT (MRC-WPT) with a single pair of transmitter and receiver has been thoroughly studied in the literature, there is limited work on the general setup with multiple transmitters and/or receivers. In this paper, we consider a point-to-multipoint MRC-WPT system with one transmitter delivering wireless power to a set of distributed receivers. We aim to introduce new applications of signal processing and optimization techniques to the performance characterization and optimization in multiuser WPT via MRC. We first derive closed-form expressions for the power drawn from the energy source at the transmitter and that delivered to the load at each receiver. We identify a "near-far" fairness issue in multiuser power transmission due to receivers' distance-dependent mutual inductance with the transmitter. To tackle this issue, we propose a centralized charging control algorithm to jointly optimize the receivers' load resistance to minimize the total transmitter power drawn while meeting the given power requirement of each individual load. For ease of practical implementation, we also devise a distributed algorithm for the receivers to adjust their load resistance independently in an iterative manner. Last, we characterize the power region that constitutes all the achievable power-tuples of the loads via controlling their adjustable resistance. In particular, we compare the power regions without versus with the time sharing of users' power transmission, where it is shown that time sharing yields a larger power region in general. Extensive simulation results are provided to validate our analysis and corroborate our study on the multiuser MRC-WPT system.
In this work, we introduce the model of employing orthogonal-frequency-division multiplexing (OFDM) for transmission over time-dispersive channels in the two-way relay network (TWRN), where two source terminals exchange their information through a relay terminal using the amplify-and-forward (AF) relaying scheme. We propose a two-phase training protocol for the channel estimation, which is compatible with the two-phase data transmission scheme. In the first phase, the two source terminals send their individual training sequences concurrently to the relay, while in the second phase the relay amplifies the received training sequences and broadcasts them to both source terminals. We propose two different types of training methods as well as develop their corresponding channel estimation algorithms at each of the two source terminals. The first type of training is <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">block</i> based, for which we first estimate the cascaded source-relay-source channels, and then design algorithms to recover the individual channels between sources and relay. The second type of training is pilot-tone (PT) based, for which we propose to directly estimate the individual channels between sources and relay. Moreover, the identifiability issues on the nonambiguous estimate of the channels in both types of proposed training are carefully addressed. Finally, various numerical examples are presented to corroborate our analytical results.
In this letter, we study the channel estimation for wireless communications with movable antenna (MA), which requires to reconstruct the channel response at any location in a given region where the transmitter/receiver is located based on the channel measurements taken at finite locations therein, so as to find the MA's location for optimizing the communication performance. To reduce the pilot overhead and computational complexity for channel estimation, we propose a new successive transmitter-receiver compressed sensing (STRCS) method by exploiting the efficient representation of the channel responses in the given transmitter/receiver region (field) in terms of multi-path components. Specifically, the field-response information (FRI) in the angular domain, including the angles of departure (AoDs)/angles of arrival (AoAs) and complex coefficients of all significant multi-path components are sequentially estimated based on a finite number of channel measurements taken at random/selected locations by the MA at the transmitter and/or receiver. Simulation results demonstrate that the proposed channel reconstruction method outperforms the benchmark schemes in terms of both pilot overhead and channel reconstruction accuracy.
Data storage is an integral part of IT infrastructures, where Quality of Service (QoS) differentiation amongst customers and their applications is essential for many. Achieving this objective in a production environment is nontrivial, because these environments are complex and dynamic. Numerous practical and engineering constraints render the task even more challenging. This paper presents SLED-2, a QoS differentiation solution that meets these challenges in offering effective protection to the performance of important workloads at the expense of less important workloads when needed. SLED-2 uses a customized feedback heuristic that rate-limits selected I/O streams. This approach is unique in that it accounts for a number of important practical considerations, including fine-grained controls, errors in storage systems models, and inexpensive and safe QoS management. SLED-2 has been implemented for the IBM DS8000 series storage servers and shown to be highly effective in a set of hostile and practical scenarios using test facilities for IBM storage products.
In recent years, wireless communication systems are expected to achieve more cost-efficient and sustainable operations by replacing conventional fixed power supplies such as batteries with energy harvesting (EH) devices, which could provide electric energy from renewable energy sources (e.g., solar and wind). Such EH power supplies, however, are random and instable in nature, and as a result impose new challenges on reliable communication design and have triggered substantial research interests in EH based wireless communications. Building upon existing works, in this article, we develop a general optimization framework to maximize the utility of EH wireless communication systems. Our framework encapsulates a variety of design problems, such as throughput maximization and outage probability minimization in single-user and multiuser setups, and provides useful guidelines to the practical design of general EH based communication systems with different assumptions over the knowledge of time-varying wireless channels and EH rates at the transmitters.
This paper addresses the design issues of the multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the licensed primary radio (PR) system. We propose a practical CR transmission strategy consisting of three major stages: environment learning, channel training, and data transmission. In the environment learning stage, the CR transceivers both listen to the PR transmission and apply blind algorithms to estimate the spaces that are orthogonal to the channels from the PR. Assuming time-division duplex (TDD) based transmission for the PR, cognitive beamforming is then designed and applied at CR transceivers to restrict the interference to/from the PR during the subsequent channel training and data transmission stages. In the channel training stage, the CR transmitter sends training signals to the CR receiver, which applies the linear-minimum-mean-square-error (LMMSE) based estimator to estimate the effective channel. Considering imperfect estimations in both learning and training stages, we derive a lower bound on the ergodic capacity achievable for the CR in the data transmission stage. From this capacity lower bound, we observe a general learning/training/throughput tradeoff associated with the proposed scheme, pertinent to transmit power allocation between training and transmission stages, as well as time allocation among learning, training, and transmission stages. We characterize the aforementioned tradeoff by optimizing the associated power and time allocation to maximize the CR ergodic capacity.
Due to the passive nature of Intelligent Reflecting Surface (IRS), channel estimation is a fundamental challenge in IRS-aided wireless networks. Particularly, as the number of IRS reflecting elements and/or that of IRS-served users increase, the channel training overhead becomes excessively high. To tackle this challenge, we propose in this paper a new anchor-assisted two-phase channel estimation scheme, where two anchor nodes, namely A1 and A2, are deployed near the IRS for helping the base station (BS) to acquire the cascaded BS-IRS-user channels. Specifically, in the first phase, the partial channel state information (CSI), i.e., the element-wise channel gain square, of the BS-IRS link is obtained by estimating the BS-IRS-A1/A2 channels and the A1-IRS-A2 channel, separately. Then, in the second phase, by leveraging such partial knowledge of the BS-IRS channel that is common to all users, the individual cascaded BS-IRS-user channels are efficiently estimated. Simulation results demonstrate that the proposed anchor-assisted channel estimation scheme is able to achieve comparable mean-squared error (MSE) performance as compared to the conventional scheme, but with significantly reduced channel training time.
On account of the on-site corrosion investigation of power transmission and transformation equipment in Sichuan's high humidity area, the reasons for corrosion of equipment in high humidity area of Sichuan were analyzed from various aspects, and an analysis model of multi-factor influence of corrosion of transmission equipment was proposed. Through the dynamic grasp of the equipment rust area map, the equipment anti-corrosion measures are proposed from three perspectives: metal anti-corrosion degree, transmission line identification and management, and line maintenance. The results show that the high sulfur and high acid wet settlement environment is the factor that accelerates the corrosion rate of equipment.
This paper studies the two-hop relay broadcast channel (BC) for a relay-assisted wireless cellular network where the multiuser independent downlink signals from the base station (BS) are first transmitted to a fixed relay station (RS), and then forwarded by the RS to multiple mobile users. Assuming both the BS and RS are equipped with multiantennas, we study the joint optimization of linear beamforming and power control at the BS and RS so as to minimize their weighted sum-power consumption under the user minimum signal-to-interference-noise-ratio (SINR) - quality-of-service (QoS) - constraints. We apply two well-known criteria in the literature, namely, the ldquoSINR balancingrdquo and the ldquochannel-inversion,rdquo for the design of linear precoding in the traditional nonrelay-assisted multiantenna BC to the relay-assisted multiantenna BC. First, a convergence-ensured iterative SINR-balancing algorithm is proposed to successively in turn optimize the transmit parameters at one station (BS or RS) with those at the other station being fixed. Second, a joint BS and RS channel-inversion algorithm is proposed together with a novel technique, termed ldquoeigenmode switching,rdquo at the RS to reduce the power penalty of the channel inversion. Simulation results show that the proposed joint beamforming and power control schemes provide substantial power savings to achieve the assigned user QoS constraints.
In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system. In particular, we propose a new successive convex approximation based second-order cone programming approach in which all the optimization variables are simultaneously updated in each iteration. Our proposed scheme achieves superior performance compared to state-of-the-art benchmark solutions. In addition, the complexity of the proposed scheme is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N_{\mathrm {s}}^{3.5})$ </tex-math></inline-formula> , while that of state-of-the-art benchmark schemes is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N_{\mathrm {s}}^{7})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N_{\mathrm {s}}$ </tex-math></inline-formula> denotes the number of reflecting elements at the IRS.
