Yanxiao Zhao

Dynamic spectrum access: from cognitive radio to network radio

Dynamic spectrum access is a new spectrum sharing paradigm that allows secondary users to access the abundant spectrum holes or white spaces in the licensed spectrum bands. DSA is a promising technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this article, we discuss the challenges of DSA and aim to shed light on its future. We first give an introduction to the state-of- the-art in spectrum sensing and spectrum sharing. Then, we examine the challenges that prevent DSA from major commercial deployment. We believe that, to address these challenges, a new DSA model is critical, where the licensed users cooperate in DSA and hence much more flexible spectrum sharing is possible. Furthermore, the future DSA model should consider the political, social, economic, and technological factors all together, to pave the way for the commercial success of DSA. To support this future DSA model, the future cognitive radio is expected to have additional components and capabilities, to enforce policy, provide incentive and coexistence mechanisms, etc. We call the future cognitive radio with the expanded capabilities a network radio, and discuss its architecture as well as the design issues for future DSA.

FMAC: A fair MAC protocol for coexisting cognitive radio networks

Cognitive radio is viewed as a disruptive technology innovation to improve spectrum efficiency. The deployment of coexisting cognitive radio networks, however, raises a great challenge to the medium access control (MAC) protocol design. While there have been many MAC protocols developed for cognitive radio networks, most of them have not considered the coexistence of cognitive radio networks, and thus do not provide a mechanism to ensure fair and efficient coexistence of cognitive radio networks. In this paper, we introduce a novel MAC protocol, termed fairness-oriented media access control (FMAC), to address the dynamic availability of channels and achieve fair and efficient coexistence of cognitive radio networks. Different from the existing MACs, FMAC utilizes a three-state spectrum sensing model to distinguish whether a busy channel is being used by a primary user or a secondary user from an adjacent cognitive radio network. As a result, secondary users from coexisting cognitive radio networks are able to share the channel together, and hence to achieve fair and efficient coexistence. We develop an analytical model using two-level Markov chain to analyze the performance of FMAC including throughput and fairness. Numerical results verify that FMAC is able to significantly improve the fairness of coexisting cognitive radio networks while maintaining a high throughput.

A weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks

Spectrum sensing plays a critical role in cognitive radio networks. A good sensing scheme can reduce the false alarm probability and the miss detection probability, and thus improves spectrum utilization. This paper presents a weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks, to increase the spectrum sensing accuracy. The framework contains two modules. In the first module, each cognitive radio performs local spectrum sensing and computes the total error probability, which combines the false alarm probability and the miss detection probability. The total error probability and the energy signal from the primary user are then sent to the base station. In the second module, the base station makes a final decision after combining the weighted energy signals from all cognitive radios. The final decision is then broadcasted back to all cognitive radios. To reduce the computation complexity and communication overhead, the base station also instructs the cognitive radios that have large total error probabilities not to report their local sensing results. We have developed a theoretical model for the proposed framework, and derived the optimal detection threshold, as well as the minimum number of cognitive radios required to participate in cooperative sensing, subject to a given total error probability. Numerical results verify that the proposed weighted cooperative spectrum sensing framework significantly improves the sensing accuracy.

Spectrum sensing based on three-state model to accomplish all-level fairness for co-existing multiple cognitive radio networks

Spectrum sensing plays a critical role in cognitive radio networks (CRNs). The majority of spectrum sensing algorithms aim to detect the existence of a signal on a channel, i.e., they classify a channel into either busy or idle state, referred to as a two-state sensing model in this paper. While this model works properly when there is only one CRN accessing a channel, it significantly limits the potential and fairness of spectrum access when there are multiple co-existing CRNs. This is because if the secondary users (SUs) from one CRN are accessing a channel, SUs from other CRNs would detect the channel as busy and hence be starved. In this paper, we propose a three-state sensing model that distinguishes the channel into three states: idle, occupied by a primary user, or occupied by a secondary user. This model effectively addresses the fairness concern of the two-state sensing model, and resolves the starvation problem of multiple co-existing CRNs. To accurately detect each state of the three, we develop a two-stage detection procedure. In the first stage, energy detection is employed to identify whether a channel is idle or occupied. If the channel is occupied, the received signal is further analyzed at the second stage to determine whether the signal originates from a primary user or an SU. For the second stage, we design a statistical model and use it for distance estimation. For detection performance, false alarm and miss detection probabilities are theoretically analyzed. Furthermore, we thoroughly analyze the performance of throughput and fairness for the three-state sensing model compared with the two-state sensing model. In terms of fairness, we define a novel performance metric called all-level fairness for all(ALFA) to characterize fairness among CRNs. Extensive simulations are carried out under various scenarios to evaluate the three-state sensing model and verify the aforementioned theoretical analysis.

A K-means-based network partition algorithm for controller placement in software defined network

Software Defined Networking (SDN), the novel paradigm of decoupling the control logic from packet forwarding devices, has been drawing considerable attention from both academia and industry. As the latency between a controller and switches is a significant factor for SDN, selecting appropriate locations for controllers to shorten the latency becomes one grand challenge. In this paper, we investigate multi-controller placement problem from the perspective of latency minimization. Distinct from previous works, the network partition technique is introduced to simplify the problem. Specifically, the network partition problem and the controller placement problem are first formulated. An optimized K-means algorithm is then proposed to address the problem. Extensive simulations are conducted and results demonstrate that the proposed algorithm can remarkably reduce the maximum latency between centroid and their nodes compared with the standard K-means. Specifically, the maximum latency can reach 2.437 times shorter than the average latency achieved by the standard K-means.

A Game-Theoretic Resource Allocation Approach for Intercell Device-to-Device Communications in Cellular Networks

Device-to-device (D2D) communication is a recently emerged disruptive technology for enhancing the performance of current cellular systems. To successfully implement D2D communications underlaying cellular networks, resource allocation to D2D links is a critical issue, which is far from trivial due to the mutual interference between D2D users and cellular users. Most of the existing resource allocation research for D2D communications has primarily focused on the intracell scenario while leaving the intercell settings not considered. In this paper, we investigate the resource allocation issue for intercell scenarios where a D2D link is located in the overlapping area of two neighboring cells. Furthermore, we present three intercell D2D scenarios regarding the resource allocation problem. To address the problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely, base stations, as players competing resource allocation quota from D2D demand, and we define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the Nash equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance, including sum rate and sum rate gain, but also shed lights on resource configurations for intercell D2D scenarios.

Resource allocation for intercell device-to-device communication underlaying cellular network: A game-theoretic approach

Device-to-Device (D2D) communication is envisioned as a promising technology to significantly improve the performance of current cellular infrastructures. Allocating resources to the D2D link, however, raises an enormous challenge to the co-existing D2D and cellular communications due to mutual interference. While there have been many resource allocation solutions proposed for D2D underlaying cellular network, they have primarily focused on the intracell scenario while leaving the intercell settings untouched. In this paper, we investigate the resource allocation problem for intercell D2D communications underlaying cellular networks, where D2D link is located in the overlapping area of two neighboring cells. We present three inter-cell D2D scenarios regarding the resource allocation problem. To address this problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely Base Stations (BSs), as players competing resource allocation quota for D2D demand, and define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance including sum rate and sum rate gain, but also sheds lights on resource configurations for intercell D2D scenarios.

The Controller Placement Problem in Software Defined Networking: A Survey

Recently, a variety of solutions have been proposed to tackle the controller placement problem in SDN. The objectives include minimizing the latency between controllers and their associated switches, enhancing reliability and resilience of the network, and minimizing deployment cost and energy consumption. In this article, we first survey the state-of-the-art solutions and draw a taxonomy based on their objectives, and then propose a new approach to minimize the packet propagation latency between controllers and switches. In order to encourage future research, we also identify the ongoing research challenges and open issues relevant to this problem.

Multicast Routing for Multimedia Communications in the Internet of Things

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.

Game theoretic resource allocation for multicell D2D communications with incomplete information

Resource allocation plays a critical role in implementing D2D communications underlaying a cellular network. Game-based approaches are recently proposed to address the resource allocation issue. Most existing approaches employ deterministic game models while implicitly assuming that each player in the game is completely willing to exchange transmission parameters with other players. Thus each player knows the complete information of all others. However, this assumption may not be satisfied in practice. For example, users may be reluctant to disclose all their parameters to peers. In this paper, we fully consider this scenario, i.e., players have incomplete information of others, and investigate the resource allocation problem for multicell D2D communications where a D2D link utilizes common resources of multiple cells. To attack this problem, a game-theoretic approach under the incomplete information condition is proposed. Specifically, we characterize the Base Stations (BSs) as players competing for resource allocation quota from the D2D demand, formulate the utility of each player as payoff from both cellular and D2D communications leasing the resources, and design the strategy for each player that is determined based on prior probabilistic payoff information of other players. We conduct extensive simulations to examine the proposed approach and the results demonstrate that the utility, sum rate, and sum rate gain of each player under the incomplete information condition are surprisingly higher than the counterparts under the complete information condition.