Search

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks. [ABSTRACT FROM AUTHOR]

In this article, we introduce Wireless 2.0, the future generation of wireless communication networks, in which the radio environment becomes controllable and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces (RMSs) and artificial intelligence (AI). In particular, we emphasize AI-based computational methods and commence with an overview of the concept of intelligent radio environments (IREs) based on RMSs. Then, we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforcement learning to learn by acting. Finally, we highlight numerous open challenges and research directions. [ABSTRACT FROM AUTHOR]

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new concept of a knowledge management framework to enable a self-optimizing and self-learning for wireless system operation in real time. The framework encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning, and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions improving the quality of future decisions. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness problem of joint channel and location optimization in a wireless system. Extensive simulations are run to validate its fast convergence, high throughput, and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to propose autonomous self-optimization with knowledge management. [ABSTRACT FROM AUTHOR]

This article discusses technology and opportunities available to embrace artificial intelligence (AI) in the design of autonomous wireless systems. A vision is presented for knowledge-driven wireless operation by means of AI disciplines, such as sensing, reasoning, knowledge management, and active learning. The aim is to provide readers with the motivation and a general big data-independent AI methodology for autonomous agents in the context of self-organization in real time by unifying knowledge management with sensing, reasoning, and active learning. Differences between training-based methods for matching problems and training-free methods for environment-specific problems are highlighted. Finally, we conceptually introduce the functions of an autonomous agent with knowledge management. [ABSTRACT FROM AUTHOR]

With the advancements of next-generation programmable networks, traditional rule-based decision- making may not be able to adapt effectively to changing network and customer requirements and provide an optimal customer experience. CEM components and implementation challenges with respect to operator, network, and business requirements must be understood to meet required demands. This article gives an overview of CEM components and their design challenges. We elaborate on data analytics and artificial intelligence driven CEM and their functional differences. This overview provides a path toward an autonomous CEM framework in next-generation networks and sets the groundwork for future enhancements. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters. [ABSTRACT FROM AUTHOR]

Wireless customers expect to have a guaranteed quality of experience at all times, at any location, and through different devices. Wi-Fi has become an access network of preference for service/ network providers and customers as well for public and private access. This sets a challenging requirement for next-generation Wi-Fi technology to provide seamless and uniform network quality of service). Consequently, the necessity for self-optimization of network and radio frequency segments becomes critical. This article surveys challenges and use cases of Wi-Fi self-optimizing networks (Wi-SONs) that have not been presented to date. We address technology and design challenges that shape Wi-SON as a very complex problem. [ABSTRACT FROM PUBLISHER]

With the increase of network complexity, there is a high need for network management automation. This is achieved through SON principles that enable self-configuration, self-optimization, and self-healing. However, even though SON functions are meant to be autonomous, a high level of coordination among them is required. To this end, efficient conflict detection and resolution techniques are needed, especially in multi-vendor deployments. This article presents a design together with a sample implementation of a coordination scheme between three key SON functions in femtocell networks: cell ID assignment, coverage adjustment, and idle mode control. This ensures stability and continuity of the network operation even in a situation when the functions have contradicting objectives. The solution is based on the Broadband Forum TR-069 protocol and is applicable to multi-vendor networks. Simulation evaluation has shown that SON coordination reduces mean cell ID conflicts by over 30 percent and, resulting from that, call drop probability by over 40 percent. [ABSTRACT FROM PUBLISHER]