Optimal Geographical Caching in Heterogeneous Cellular Networks

We investigate optimal geographical caching in heterogeneous cellular networks, where different types of base stations (BSs) have different cache capacities. The content library contains files with different popularities. The performance metric is the total hit probability. The problem of optimally placing content in all BSs jointly is not convex in general. However, we show that when BSs are deployed according to homogeneous Poisson point processes (PPP), independently for each type, we can formulate the problem as a convex problem. We give the optimal solution to the joint problem for PPP deployment. For the general case, we provide a distributed local optimization algorithm (LOA) that finds the optimal placement policies for different types of BSs. We find the optimal placement policy of the small BSs (SBSs) depending on the placement policy of the macro BSs (MBSs). We show that storing the most popular content in the MBSs is almost optimal if the SBSs are using an optimal placement policy. Also, for the SBSs no such heuristic can be used; the optimal placement is significantly better than storing the most popular content. Finally, we numerically verify that LOA gives the same hit probability as the joint optimal solution for the PPP model.Comment: 30 pages, 7 figures, extended version of arXiv:1601.07322, submitted to IEEE Transactions on Wireless Communication

Dynamic spectrum sharing game by lease

We propose and analyze a dynamic implementation of the property-rights model of cognitive radio. A primary link has the possibility to lease the owned spectrum to a MAC network of secondary nodes, in exchange for cooperation in the form of distributed space-time coding (DSTC). The cooperation and competition between the primary and secondary network are cast in the framework of sequential game. On one hand, the primary link attempts to maximize its quality of service in terms of signal-to-interference-plus-noise ratio (SINR); on the other hand, nodes in the secondary network compete for transmission within the leased time-slot following a power control mechanism. We consider both a baseline model with complete information and a more practical version with incomplete information, using the backward induction approach for the former and providing approximate algorithm for the latter. Analysis and numerical results show that our models and algorithms provide a promising framework for fair and effective spectrum sharing, both between primary and secondary networks and among secondary nodes.Comment: 15 pages, 4 figures, 1 table. Revisio

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Small Cell Deployments: Recent Advances and Research Challenges

This paper summarizes the outcomes of the 5th International Workshop on Femtocells held at King's College London, UK, on the 13th and 14th of February, 2012.The workshop hosted cutting-edge presentations about the latest advances and research challenges in small cell roll-outs and heterogeneous cellular networks. This paper provides some cutting edge information on the developments of Self-Organizing Networks (SON) for small cell deployments, as well as related standardization supports on issues such as carrier aggregation (CA), Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell Interference Coordination (eICIC), etc. Furthermore, some recent efforts on issues such as energy-saving as well as Machine Learning (ML) techniques on resource allocation and multi-cell cooperation are described. Finally, current developments on simulation tools and small cell deployment scenarios are presented. These topics collectively represent the current trends in small cell deployments.Comment: 19 pages, 22 figure

Cognitive Internet of Things: A New Paradigm beyond Connection

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain' for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning

Relay Assisted Device-to-Device Communication: Approaches and Issues

Enabling technologies for 5G and future wireless communication have attracted the interest of industry and research communities. One of such technologies is Device-to-Device (D2D) communication which exploits user proximity to offer spectral efficiency, energy efficiency and increased throughput. Data offloading, public safety communication, context aware communication and content sharing are some of the use cases for D2D communication. D2D communication can be direct or through a relay depending on the nature of the channel in between the D2D devices. Apart from the problem of interference, a key challenge of relay aided D2D communication is appropriately assigning relays to a D2D pair while maintaining the QoS requirement of the cellular users. In this article, relay assisted D2D communication is reviewed and research issues are highlighted. We also propose matching theory with incomplete information for relay allocation considering uncertainties which the mobility of the relay introduces to the set up

Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks

This paper aims at answering two fundamental questions: how area spectral efficiency (ASE) behaves with different system parameters; how to design an energy-efficient network. Based on stochastic geometry, we obtain the expression and a tight lower-bound for ASE of Poisson distributed networks considering multi-user MIMO (MU-MIMO) transmission. With the help of the lower-bound, some interesting results are observed. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another work of this paper is joint optimization of the base station (BS) density, the number of antennas and active users to minimize the network energy consumption. It is discovered that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near optimal performance.Comment: Submitted to IEEE Transactions on Wireless Communications, Major Revisio

Heterogeneous Services Provisioning in Small Cell Networks with Cache and Mobile Edge Computing

In the area of full duplex (FD)-enabled small cell networks, limited works have been done on consideration of cache and mobile edge communication (MEC). In this paper, a virtual FD-enabled small cell network with cache and MEC is investigated for two heterogeneous services, high-data-rate service and computation-sensitive service. In our proposed scheme, content caching and FD communication are closely combined to offer high-data-rate services without the cost of backhaul resource. Computing offloading is conducted to guarantee the delay requirement of users. Then we formulate a virtual resource allocation problem, in which user association, power control, caching and computing offloading policies and resource allocation are jointly considered. Since the original problem is a mixed combinatorial problem, necessary variables relaxation and reformulation are conducted to transfer the original problem to a convex problem. Furthermore, alternating direction method of multipliers (ADMM) algorithm is adopted to obtain the optimal solution. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme

Fundamentals of Simultaneous Wireless Information and Power Transmission in Heterogeneous Networks: A Cell Load Perspective

In a heterogeneous cellular network (HetNet) consisting of multiple different types (tiers) of base stations (BSs), the void cell event in which a BS does not have any users has been shown to exist due to user-centric BS association and its probability is dominated by the cell load of each tier. Such a void cell phenomenon has not been well characterized in the modeling and analytical framework of simultaneous wireless information and power transmission (SWIPT) in a HetNet. This paper aims to accurately exploit the fundamental performance limits of the SWIPT between a BS and its user by modeling the cell-load impact on the downlink and uplink transmissions of each BS. We first characterize the power-splitting receiver architecture at a user and analyze the statistical properties and limits of its harvested power and energy, which reveals how much of the average energy can be harvested by users and how likely the self-powered sustainability of users can be achieved. We then derive the downlink and uplink rates that characterize the cell-load and user association effects and use them to define the energy efficiency of a user. The optimality of the energy efficiency is investigated, which maximizes the SWIPT performance of the receiver architecture for different user association and network deployment scenarios.Comment: 16 pages, 6 figures, 1 table, journal articl

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems