Throughput maximization in linear multiuser MIMO-OFDM downlink systems

In this paper, we study the problem of maximizing the throughput of a multiuser multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system in the downlink with a total power constraint using a beamforming approach. An iterative algorithm that takes turns to optimize, jointly among users, the power allocation in the downlink, the transmit and the receive beamforming antenna vectors, and the power allocation in the virtual uplink is proposed. The algorithm is proved to converge, and the throughput increases from one iteration to the next. In addition to the total power constraint, the proposed algorithm is also capable of handling individual users' rate constraints. To reduce complexity, a geometric-programming-based power control in the high signal-to-interference-plus-noise ratio (SINR) region and an orthogonal frequency-division multiple-access scheme in the low SINR region are proposed. Numerical results illustrate that the proposed algorithm significantly outperforms the generalized zero-forcing (GZF) approach. © 2008 IEEE.published_or_final_versio

Energy-efficient multiuser SIMO: Achieving probabilistic robustness with Gaussian channel uncertainty

This paper addresses the joint optimization of power control and receive beamforming vectors for a multiuser singleinput multiple-output (SIMO) antenna system in the uplink in which mobile users are single-antenna transmitters and the base station receiver has multiple antennas. Channel state information at the receiver (CSIR) is exploited but the CSIR is imperfect with its uncertainty being modeled as a random Gaussian matrix. Our objective is to devise an energy-efficient solution to minimize the individual users' transmit power while meeting the users' signal-to-interference plus noise ratio (SINR) constraints, under the consideration of CSIR and its error characteristics. This is achieved by solving a sum-power minimization problem, subject to a collection of users' outage probability constraints on their target SINRs. Regarding the signal power minus the sum of inter-user interferences (SMI) power as Gaussian, an iterative and convergent algorithm which is proved to reach the global optimum for the joint power allocation and receive beamforming solution, is proposed, though the optimization problem is indeed non-convex. A systematic scheme to detect feasibility and find a feasible initial solution, if there exists any, is also devised. Simulation results verify the use of Gaussian approximation and robustness of the proposed algorithm in terms of users' probability constraints, and indicate a significant performance gain as compared to the zero-forcing (ZF) and minimum meansquare-error (MMSE) beamforming systems. © 2009 IEEE.published_or_final_versio

Increasing external effects negate local efforts to control ozone air pollution: a case study of Hong Kong and implications for other Chinese cities.

It is challenging to reduce ground-level ozone (O3) pollution at a given locale, due in part to the contributions of both local and distant sources. We present direct evidence that the increasing regional effects have negated local control efforts for O3 pollution in Hong Kong over the past decade, by analyzing the daily maximum 8 h average O3 and Ox (=O3+NO2) concentrations observed during the high O3 season (September-November) at Air Quality Monitoring Stations. The locally produced Ox showed a statistically significant decreasing trend over 2002-2013 in Hong Kong. Analysis by an observation-based model confirms this decline in in situ Ox production, which is attributable to a reduction in aromatic hydrocarbons. However, the regional background Ox transported into Hong Kong has increased more significantly during the same period, reflecting contributions from southern/eastern China. The combined result is a rise in O3 and a nondecrease in Ox. This study highlights the urgent need for close cross-boundary cooperation to mitigate the O3 problem in Hong Kong. China's air pollution control policy applies primarily to its large cities, with little attention to developing areas elsewhere. The experience of Hong Kong suggests that this control policy does not effectively address secondary pollution, and that a coordinated multiregional program is required

Robust beamforming in cognitive radio

In cognitive radio, it is crucial to control the interference from secondary users (SUs) to primary users (PUs). This paper studies the use of transmit beamforming in the cognitive secondary network for enhancing the performance of a SU while controlling the interference to the PUs. In particular, we propose to maximize the service probability of the SU with a number of probability constraints on the interference level at the PUs with the aid of imperfect channel state information (CSI). Modeling the CSI uncertainty as an additive Gaussian noise, it is shown that the optimum can be realized by second-order cone-programming (SOCP) in tandem with a one-dimensional search. Results reveal that the proposed approach provides a technique to tradeoff the performance between the PUs and the SU, making an analytical connection between non-robust and worst-case systems. © 2009 IEEE.published_or_final_versionThe 69th IEEE Vehicular Technology Conference (VTC Spring 2009), Barcelona, Spain, 26-29 April 2009. In Proceedings of the 69th IEEE - VTS, Spring 2009, p. 1-

Robust precoder design in MISO downlink based on quadratic channel estimation

In [1], it has been proposed that channel estimates in quadratic form can be obtained at the base station by sending training sequences to the mobiles where the received signals are forwarded back to the base for channel estimation. In this paper, we first examine the optimal training sequence design for such quadratic channel estimation and then analyze the error bound and statistics of the channel estimates in quadratic form. With the analytical results, two problems for a multiple-input single-output (MISO) antenna system in the downlink are constructed and optimally solved: Power minimization with individual users' 1) worst-case signal-to-interference plus noise ratio (SINR) and 2) average mean-square-error (MSE) constraints, through optimal multiuser MISO beamforming and power allocation. ©2008 IEEE.published_or_final_versio

Robust beamforming in cognitive radio

This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worstcase based systems. © 2010 IEEE.published_or_final_versio

Physical Layer Security in Large-Scale Random Multiple Access Wireless Sensor Networks: A Stochastic Geometry Approach

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint

Fast Meta Learning for Adaptive Beamforming

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance

The Synergy of Edge and Central Cloud Computing with Wireless MIMO Backhaul

In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only

Multi-Agent Collaborative Learning for UAV Enabled Wireless Networks

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes