In this paper, a cross-tier cooperation framework in H-CRAN is proposed to mitigate inter-tier interference. Specifically, a small cell remote radio head (S-RRH) acts as the relay for multiple macrocell users (MUEs), and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving small cell users (SUEs) which are secondary users, while the MUEs which are primary users can improve their data transmission utility. The cooperation problem is formulated as a mixed integer programming, which is NP-hard. To solve this problem, we transform the cooperation problem into a joint cooperation time and power allocation problem and a cooperator selection problem. First, we solve the joint cooperation time and power allocation problem to obtain the maximum cooperation utility of all the possible cooperating pairs. We derive the closed-form solution for the allocation problem by using linear independence constraint qualification and Karush-Kuhn-Tucker conditions. Then, we solve the cooperator selection problem by a two-sided matching algorithm. Extensive simulation results show that the utility of macrocell networks and small cell networks can be improved by adopting the proposed cooperation framework, and the cooperator selection result is stable and close-to-optimal.

In this paper, we propose a cooperation framework in heterogeneous cloud radio access networks (H-CRANs) to mitigate inter-tier interference. Specifically, small cell remote radio head (S-RRH) acts as the cognitive relay for multiple macrocell users (MUEs) which are primary users, and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving secondary users-small cell users (SUEs), while the MUEs can improve their transmission rates. Moreover, the inter-tier interference between macrocell networks and small cell networks can be mitigated via cooperation. The cooperation problem is formulated as a binary integer programming problem which is NP-hard. To solve this problem, we transform it to an equivalent many-to-one matching problem. Then, we achieve the near optimal solution by proposing a two-sided cooperator selection algorithm, which takes the benefits of both S-RRHs and MUEs into consideration. Simulation results show that the performance of the macrocell networks as well as small cell networks can be improved by adopting the proposed scheme, and the cooperator selection result is stable and close to the optimal solution.

One of the major challenges, which prevents the coordinated multipoint (CoMP) communications concept from being widely deployed in new cellular systems, is timing synchronization. In order to achieve the gains promised by CoMP systems, the user equipment's signals in uplink (UL) or the base stations' signals in downlink should be synchronized, such that the time difference of arrivals do not exceed the cyclic prefix length of the transmitted signals; otherwise, the system suffers from unavoidable integer time offsets. These offsets lead to asynchronous interference in terms of inter-carrier, inter-block, and inter-symbol interference. This limits the percentage of area within a cell which can be covered by cooperation and imposes an upper bound on this area. In this paper, we investigate this problem in the UL, and by using geometrical and semi-analytical approaches, we define this upper bound. Also, by characterizing an accurate mathematical model for the asynchronous interference in Rayleigh fading channels, we were able to employ a standard MMSE-based receiver that mitigates this interference. Furthermore, a typical joint channel and delay estimation block is incorporated into the receiver to examine its performance with estimation errors.

Predicting the probabilities that a mobile user will be active in other cells at future moments in a cellular system is an important issue in cellular networks. With this information, mobile switching centers can predict future resource demands and assist base stations to maintain a balance between guaranteeing quality of service (QoS) to mobile users and maintaining maximum resource utilization. This chapter describes a novel adaptive fuzzy logic inference system to estimate and predict the probability information for direct sequence code division multiple access (DS/CDMA) wireless communications networks. The estimation is based on measured pilot signal strengths at the mobile users from a number of the nearby base stations, and the prediction is obtained with recursive least square (RLS) algorithm. Numerical results are presented to demonstrate the performance of the proposed technique under various path losses and channel-shadowing conditions. The proposed technique can achieve simplicity, accuracy, and low cost.

The spectrum between 30 and 300 GHz is referred to as the millimeter wave (mmWave) band because the wavelengths for these frequencies are in the range from about one to ten millimeters. The Federal Communications Commission (FCC) has allocated the 57–64 GHz mmWave band for general unlicensed use, opening the door to supporting high data rate wireless applications over the 7 GHz unlicensed band. Given the spectrum deficiency and network densification of cellular systems, how to use the mmWave band to support various machine/human-to-machine/human communications is critically important for fifth generation (5G) cellular systems.

This paper studies spectrum sharing for providing better quality of experience in 5G networks, which are characterized by multidimensional heterogeneity in terms of spectrum, cells, and user requirements. Specifically, spectrum access, power allocation, and user scheduling are jointly investigated and an optimization problem is formulated with the objective of maximizing the users' satisfaction across the network. In order to reduce the complexity and overhead, decentralized solutions with local information are required. To this end, we employ game-theoretic approach and interference graph to solve the problem. The proposed game is proved to have at least one Nash Equilibrium (NE), corresponding to either the globally or locally optimal solution to the original optimization problem. A concurrent best-response iterative algorithm is first devised to find the solution, which can converge to an NE, but may not be globally optimal. Therefore, a spatial adaptive play iterative (SAPI) learning algorithm is further proposed to search the global optimum. Theoretical analysis demonstrates that the SAPI algorithm can guarantee to find the globally optimal solution with an arbitrary large probability, when the learning step is set to be sufficiently large. Simulation results are provided to validate the performance of the proposed algorithms.

This SpringerBrief examines the active cooperation between users of Cooperative Cognitive Radio Networking (CCRN), exploring the system model, enabling techniques, and performance. The brief provides a systematic study on active cooperation between primary users and secondary users, i.e., (CCRN), followed by the discussions on research issues and challenges in designing spectrum-energy efficient CCRN. As an effort to shed light on the design of spectrum-energy efficient CCRN, they model the CCRN based on orthogonal modulation and orthogonally dual-polarized antenna (ODPA). The resource allocation issues are detailed with respect to both models, in terms of problem formulation, solution approach, and numerical results. Finally, the optimal communication strategies for both primary and secondary users to achieve spectrum-energy efficient CCRN are analyzed.

With the rapid progress in communication technologies and the explosive proliferation of wireless applications, the demand for wireless broadband services continues to explode. On the one hand, as a precious natural resource, the amount of most easily explorable radio spectrum for wireless communications is extremely limited. On the other hand, the significant spectrum underutilization resulting from current fixed spectrum allocation polices has even exacerbated the situation of spectrum scarcity.

In this paper, we propose a heterogeneous framework to deliver smart grid (SG) data cost effectively. The data generated by distributed SG loads, and generation units should be delivered to the utility control center (UCC) within the tolerated delay, which is crucial for SG applications. To this end, a heterogeneous communication framework is proposed, where the cellular network (CN) provides ubiquitous yet expensive data transmission, and vehicle-assisted device-to-device (D2D) communications are leveraged to offload the CN by delivering the delay-tolerant SG data in a store-carry-forward fashion with low cost. To improve the offloading and cost performance of the proposed framework, we put effort in the following aspects: 1) optimal forwarding schemes to optimally select vehicles to carry and forward the data; and 2) mode selection and dynamic resource allocation to maximize the amount of data delivered by D2D communications, reduce the cost of SG data delivery, and guarantee the fairness among SG users. Simulation results are given to validate proposed approaches and demonstrate that the proposed framework is efficient in saving cost for the utility and offloading the CN.

In this chapter, a cross-layer two-phase time division multiple access (TDMA) cooperation framework for primary users (PUs) and secondary user (SUs) in a cooperative cognitive radio network (CCRN) is proposed and analyzed. Specifically, the cooperation framework in which the SU uses the two-dimensional orthogonal modulation for leveraging two degrees of freedom to relay the PU’s packet and transmit its own data orthogonally in the same time slot is firstly explored. To evaluate the cooperation performance of the proposed framework, a weighted sum throughput maximization problem is then formulated. With the help of primal-dual sub-gradient algorithms, the optimization problem is solved to obtain closed-form solutions to the optimal powers and allocation of the PU and the SU for both the amplify-and-forward (AF) and decode-and-forward (DF) relaying modes. Cooperative regions based on channel state information are given and discussed, and a cross-layer multi-user coordination for a PU to select a relaying SU for both AF and DF are presented. Extensive simulation results validate the theoretical analysis and show that the proposed two-phase TDMA cooperation framework can achieve mutual benefit in the CCRN.

This paper studies downlink resource allocation to improve user experience in ultra-dense small cell networks, which is characterized by multi-dimensional heterogeneities in terms of spectrum, cells, and user requirements. Specifically, spectrum access, power allocation, and user scheduling of small cells, are jointly investigated and an optimization problem is formulated, with the objective of maximizing the users' satisfaction across the network. In order to reduce the complexity and overhead, distributed solutions with local information are required. To this end, we employ game-theoretic approach and interference graph to re-formulate the problem. We prove the existence of Nash Equilibrium (NE) in the game, which corresponds to the global or local optimum of the original problem. A concurrent best response iterative (CBSI) algorithm is proposed, which can guarantee the convergence to an NE. Simulation results are presented to validate and evaluate the performance of the proposed algorithm.

In this paper, a cluster-based two-phase coordination scheme for cooperative cognitive radio networks is proposed considering both spectrum efficiency and network fairness. Specifically, candidate secondary users SUs are first selected by a partner selection algorithm to enter the two-phase cooperation with primary users PUs. In phase I, the selected SUs cooperate with PUs to acquire a fraction of time slot as a reward. In phase II, all SUs including the unselected ones share the available spectrum resources in local clusters; each of which is managed by a cluster head who participated in the cooperation in phase I. To improve the total network utility of both PUs and SUs, the maximum weighted bipartite matching is adopted in partner selection. To further improve the network performance and communication reliability, network coding is exploited during the spectrum sharing within the cluster. Simulation results demonstrate that, with the proposed cluster-based coordination scheme, not only the PUs' transmission performance is improved, but also SUs achieve spectrum access opportunities. Copyright © 2015John Wiley & Sons, Ltd.

Millimeter wave mmWave communication is a promising technology to support high-rate e.g., multi-Gbps multimedia applications because of its large available bandwidth. Multipacket reception is one of the important capabilities of mmWave networks to capture a few packets simultaneously. This capability has the potential to improve medium access control layer performance. Because of the severe propagation loss in mmWave band, traditional backoff mechanisms in carrier sensing multiple access/collision avoidance CSMA/CA designed for narrowband systems can result not only in unfairness but also in significant throughput reduction. This paper proposes a novel backoff mechanism in CSMA/CA by giving a higher transmission probability to the node with a transmission failure than that with a transmission success, aiming to improve the system throughput. The transmission probability is adjusted by changing the contention window size according to the congestion status of each node and the whole network. The analysis demonstrates the effectiveness of the proposed backoff mechanism on reducing transmission collisions and increasing network throughput. Extensive simulations show that the proposed backoff mechanism can efficiently utilize network resources and significantly improve the network performance on system throughput and fairness. Copyright © 2014 John Wiley & Sons, Ltd.

In this article, we study how to efficiently utilize heterogeneous network resources for various service provisioning in LTE networks with unlicensed bands. Our focus is on traffic steering, which is to intelligently distribute traffic among heterogenous cells, radio access technologies, and spectrum bands based on the desires of the network or users. We first highlight the significance of traffic steering, and then present the typical applications and approaches for traffic steering. After that, discussions on how to leverage unlicensed bands for traffic steering are provided, followed by case studies to demonstrate the benefits of traffic steering. Finally, some research issues essential for traffic steering in LTE networks with unlicensed bands are identified.

This chapter is concerned with enhancement of spectrum efficiency/utilization by using polarization enabled two-phase cooperation between primary users (PUs) and secondary users (SUs) for cooperative cognitive radio networking (CCRN). Specifically, we aim to exploit the degrees of freedom provided by orthogonally dual-polarized antennas (ODPAs) to attain an interference-free two-phase cooperation framework. The use of ODPAs enables concurrent transmissions of multiple independent signals of PUs and SUs, and interference suppression via polarization zero-forcing and polarization filtering to obtain significant performance improvement. By leveraging both temporal and polarization domains, a polarization based two-timescale CCRN scheme to improve spectrum efficiency/utilization is presented. To maximize a weighted sum throughput of PUs and SUs under energy/power constraints, the problem is formulated and solved based on a multi-timescale Markov decision process, and two modified backward iteration algorithms are devised to attain the optimal policies. Numerical and simulation results validate the effectiveness of the proposed framework for CCRN, showing that the obtained policy outperforms both greedy and random ones.

Radio spectrum underutilization and energy inefficiency become urgent bottleneck problems to the sustainable development of wireless technologies. The research philosophies of wireless communications have been shifted from balancing reliability-efficiency tradeoff in the link level to seeking spectrum-energy efficiency in the network level. Global spectrum-energy efficient designs attract significant attention to improving utilization and efficiency, wherein cognitive radio networks and energy-efficient resource allocation are of particular interests. In this chapter, the authors first provide a systematic study on Cooperative Cognitive Radio Networking (CCRN). As an effort to shed light on addressing spectrum-energy inefficiency at a low complexity, an orthogonal modulation enabled two-phase cooperation framework and an Orthogonally Dual-Polarized Antenna (ODPA) based framework, as well as their resource allocation problems are given and tackled.

In this paper, we study opportunistic traffic offloading in a vehicular environment, where the cellular traffic of vehicular users (VUs) is offloaded through carrier-WiFi networks deployed by the mobile network operator (MNO). By jointly considering users' satisfaction, the offloading performance, and the MNO's revenue, two WiFi offloading mechanisms are proposed: auction game-based offloading (AGO) and congestion game-based offloading (CGO). Moreover, we introduce an approach to predict WiFi offloading potential and access cost and incorporate it in the offloading mechanisms. Specifically, with the AGO mechanism, the MNO employs auctions to sell WiFi access opportunities; VUs decide whether to bid according to their utilities and are capable of using WiFi if the auction is won. With the CGO mechanism, a VU calculates utility considering other VUs' strategies and makes offloading decisions accordingly. We show that the AGO mechanism can maximize social welfare and increase the MNO's revenue, whereas the CGO mechanism can achieve a better performance of average VU utility and fairness. Additionally, both AGO and CGO mechanisms can improve the overall WiFi offloading performance. Through simulations, we demonstrate that both AGO and CGO mechanisms can achieve higher average utility of VUs and lower average service delay and offload much more cellular traffic compared with existing offloading mechanisms.

In this paper, we investigate the cost-effectiveness of a Wi-Fi solution for vehicular Internet access. We define the cost-effectiveness as the cost saving by deploying and operating a low-cost Wi-Fi infrastructure instead of a costly benchmark cellular network. To characterize the service quality of Wi-Fi deployment, we also define the normalized service delay (NSD), which is the service time to fulfill a data application via the Wi-Fi network normalized by that via the cellular network. To derive the service time, we analyze the average throughput capacity of a generic vehicle in the Wi-Fi network and the average downlink capacity in the cellular network. In particular, we propose deploying Wi-Fi access point (AP) at signalized intersection and study the fundamental influence of traffic signals (which yield an interrupted vehicle traffic) on Wi-Fi access. Then, we examine the tradeoff between cost-effectiveness and NSD by identifying interplays between controllable (e.g., the density of Wi-Fi deployment and user's satisfaction) and uncontrollable parameters (e.g., vehicle traffic statistics). Our results are very useful for network operators to make strategic planning of Wi-Fi deployment for vehicular Internet Access.