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Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design in wireless networks is becoming more and more important. In this paper, the fundamental relation between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and peruser quality-of-service (QoS) are all considered, and prove that EE is strictly quasiconcave in SE. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results corroborate the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.Index Terms-Energy efficiency (EE), green radio (GR), orthogonal frequency division multiple access (OFDMA), spectral efficiency (SE) *

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With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: spectrum efficiency (SE) versus energy efficiency (EE), deployment efficiency (DE) versus energy efficiency (EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing (OFDM) and non-orthogonal aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous networks (HetNets).We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.Along with the dramatic traffic explosion, it is also promising to see that the next generation (5G) wireless communications shall include people-to-machine and machine-to-machine communications [23] in order to facilitate more flexible networked social information sharing. As a result, numerous sensors, accessories, or even tools may become the communication entities and the associated running applications over wireless networks will diverge. As reported in [24], wireless communications shall support up to millions of applications and billions of subscribers by year 2020, which is nearly 100 times of today's network. Consequently, with the surprisingly expanding demands for wireless transmission and supporting equipment, the network power consumption is no longer sustainable and the green radio technology becomes essential [25]. A flagship 5G research project from European Union, named Mobile April 28, 2016 DRAFT denote SE, EE and DE, respectively 2 .1 Since we mainly focus on the physical layer transmission, we only adopt the transmission delay here to characterize the delay-power tradeoff which can be regarded as a theoretical limit. 2 We note that area energy efficiency is also another important performance metric for cellular networks. Since it is more related with the cell size, we will emphasize it in heterogeneous networks in Section V. April 28, 2016 DRAFT In the practical systems, however, the tradeoff curves may behave differently. For example, if the circuit power consumption, P c , is considered in the EE evaluation, then η EE = W P +Pc log 2 1 + P W N 0 and the corresponding SE-EE/DE-EE tradeoff curves will be with a bell shape [16] as shown in Fig. 1. BW-PW/DL-PW tradeoffs under the circuit power assumptions have been also discussed in [16]. In particular, several open issues have been raised in [16], including tradeoff analysis for multi-cell systems and HetNet architectures.Four fundamental gr...

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated.Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems. Index TermsDelay-aware resource control, large deviation theory, Lyapunov stability, Markov decision process, stochastic learning.

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Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noiselike nature and may be produced by a wide variety of sources. In this paper, we propose to use a novel deep convolutional neural network for ESC tasks. Our network architecture uses stacked convolutional and pooling layers to extract high-level feature representations from spectrogram-like features. Furthermore, we apply mixup to ESC tasks and explore its impacts on classification performance and feature distribution. Experiments were conducted on UrbanSound8K, ESC-50 and ESC-10 datasets. Our experimental results demonstrated that our ESC system has achieved the state-of-the-art performance (83.7%) on UrbanSound8K and competitive performance on ESC-50 and ESC-10.