Yiping Xing

Dynamic Spectrum Access with QoS and Interference Temperature Constraints

Spectrum is one of the most precious radio resources. With the increasing demand for wireless communication, efficiently using the spectrum resource has become an essential issue. With the Federal Communications Commissions (FCC) spectrum policy reform, secondary spectrum sharing has gained increasing interest. One of the policy reforms introduces the concept of an interference temperature - the total allowable interference in a spectral band. This means that secondary users can use different transmit powers as long as the sum of these power is less than the interference threshold. In this paper, we study two problems in secondary spectrum access with minimum signal to interference noise ratio (quality of service (QoS)) guarantee under an interference temperature constraint. First, when all the secondary links can be supported, a nonlinear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The nonlinear optimization is solved efficiently using geometric programming techniques. The second problem we address is, when not all the secondary links can be supported with their QoS requirement, it is desirable to have the spectrum access opportunity proportional to the user priority if they belong to different priority classes. In this context, we formulate an operator problem which takes the priority issues into consideration. To solve this problem, first, we propose a centralized reduced complexity search algorithm to find the optimal solution. Then, in order to solve this problem distributively, we define a secondary spectrum sharing potential game. The Nash equilibria of this potential game are investigated. The efficiency of the Nash equilibria solutions are characterized. It is shown that distributed sequential play and an algorithm based on stochastic learning attain the equilibrium solutions. Finally, the performances are examined through simulations

Dynamic spectrum access in open spectrum wireless networks

One of the reasons for the limitation of bandwidth in current generation wireless networks is the spectrum policy of the Federal Communications Commission (FCC). But, with the spectrum policy reform, open spectrum wireless networks, and spectrum agile radios are set to drive next general wireless networks. In this paper, we investigate continuous-time Markov models for dynamic spectrum access in open spectrum wireless networks. Both queueing and no queueing cases are considered. Analytical results are derived based on the Markov models. A random access protocol is proposed that is shown to achieve airtime fairness. A distributed version of this protocol that uses only local information is also proposed based on homo egualis anthropological model. Inequality aversion by the radio systems to achieve fairness is captured by this model. These protocols are then extended to spectrum agile radios. Extensive simulation results are presented to compare the performances of fixed versus agile radios.

Price dynamics in competitive agile spectrum access markets

We explore the price dynamics in a competitive market consisting of spectrum agile network service providers and users. Here, multiple self interested spectrum providers operating with different technologies and costs compete for potential customers. Different buyers or consumers may evaluate the same seller differently depending on their applications, operating technologies and locations. Two different buyer populations, the quality-sensitive and the price-sensitive are investigated, and the resulting collective price dynamics are studied using a combination of analysis and simulations. Various scenarios are considered regarding the nature and accuracy of information available to the sellers. A myopically optimal strategy is studied when full information is available, while a stochastic learning based strategy is considered when the information is limited. Cooperating groups may be formed among the sellers which will in-turn influence the group profit for those participants. Free riding phenomenon is observed under certain circumstances

Reliable Multimedia Transmission Over Cognitive Radio Networks Using Fountain Codes

With the explosive growth of wireless multimedia applications over the wireless Internet in recent years, the demand for radio spectral resources has increased significantly. In order to meet the quality of service, delay, and large bandwidth requirements, various techniques such as source and channel coding, distributed streaming, multicast etc. have been considered. In this paper, we propose a technique for distributed multimedia transmission over the secondary user network, which makes use of opportunistic spectrum access with the help of cognitive radios. We use digital fountain codes to distribute the multimedia content over unused spectrum and also to compensate for the loss incurred due to primary user interference. Primary user traffic is modelled as a Poisson process. We develop the techniques to select appropriate channels and study the trade-offs between link reliability, spectral efficiency and coding overhead. Simulation results are presented for the secondary spectrum access model.

Stochastic learning solution for distributed discrete power control game in wireless data networks

Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and Qualcomms proposal to the IS-95 standard use a finite number of discretized power levels. This motivates the need to investigate solutions for distributed discrete power control which is the primary objective of this paper. We first note that, by simply discretizing, the previously proposed continuous power adaptation techniques will not suffice. This is because a simple discretization does not guarantee convergence and uniqueness. We propose two probabilistic power adaptation algorithms and analyze their theoretical properties along with the numerical behavior. The distributed discrete power control problem is formulated as an N-person, nonzero sum game. In this game, each user evaluates a power strategy by computing a utility value. This evaluation is performed using a stochastic iterative procedures. We approximate the discrete power control iterations by an equivalent ordinary differential equation to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium or even only mixed equilibrium may exist are also studied. Experimental results are presented for several cases and compared with the continuous power level adaptation solutions.

Distributed discrete power control for bursty transmissions over wireless data networks

Distributed power control is an important issue in wireless networks. Recently, due to the bursty nature of data communication, packet switching is used in cellular systems. In addition, majority of previous power control algorithm assume that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and QUALCOMMs proposal to the IS-95 standard use a finite number of discretized power levels. These motivate the need to investigate solutions for distributed discrete power control for bursty transmission. Therefore, we propose a probabilistic power adaptation algorithm and analyze its theoretical properties along with the numerical behavior for bursty transmission. We approximate the discrete power control iterations by an equivalent ordinary differential equation (ODE) to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium may exist are also studied. Experimental results are presented for several cases and the impact of data burstiness on the proposed algorithm is also concerned.

Priority Based Dynamic Spectrum Access with QoS and Interference Temperature Constraints

Spectrum is one of the most precious radio resources. With the increasing demand for wireless communication, efficiently using the spectrum resource has become an essential issue. With the Federal Communications Commissions (FCC) spectrum policy reform, secondary spectrum sharing has gained increasing interest. One of the policy reforms introduces the concept of an interference temperature - the total allowable interference in a spectral band. This means that secondary users can use different transmit powers as long as the sum of these power is less than the interference threshold. In this paper, we study two problems in secondary spectrum access with minimum signal to interference noise ratio (quality of service (QoS)) guarantee under an interference temperature constraint. First, when all the secondary links can be supported, a nonlinear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The nonlinear optimization is solved efficiently using geometric programming techniques. The second problem we address is, when not all the secondary links can be supported with their QoS requirement, it is desirable to have the spectrum access opportunity proportional to the user priority if they belong to different priority classes. In this context, we formulate an operator problem which takes the priority issues into consideration. To solve this problem, first, we propose a centralized reduced complexity search algorithm to find the optimal solution. Then, in order to solve this problem distributively, we define a secondary spectrum sharing potential game. The Nash equilibria of this potential game are investigated. The efficiency of the Nash equilibria solutions are characterized. It is shown that distributed sequential play and an algorithm based on stochastic learning attain the equilibrium solutions. Finally, the performances are examined through simulations

Analysis and performance evaluation of a fair channel access protocol for open spectrum wireless networks

Wireless multimedia services currently suffer due to limited bandwidth. One of the reasons for this limitation was the Federal Communications Commissions (FCC) spectrum regulation policy. But, with the advantage of open spectrum wireless networking the bandwidth constraint is minimized. In this paper, a Markov model is presented and used to analyze the unlicensed band access. Then a random access scheme is proposed which provisions the existing etiquettes with more efficiency and fairness. Later a homo egualis society model based access scheme is built to implement the random access scheme in a distributed fashion. Simulations are performed to demonstrate the accuracy of this Markov model and to show the efficiency and fairness of the proposed schemes.

Codes and Games for Dynamic Spectrum Access

ly, a learning automaton [2] can be considered to be an object that can choose from a finite number of actions. For every action that it chooses, the random environment in which it operates evaluates that action. A corresponding feedback is sent to the automaton based on which the next action is chosen. As this process progresses the automaton learns to choose the optimal action for that unknown environment asymptotically. The stochastic iterative algorithm used by the automaton to select its successive actions based on the environment’s response defines the stochastic learning algorithm. An important property of the learning automaton is its ability to improve its performance with time while operation in an unknown environment. In this chapter, for the sake of consistency our notations follow or parallels that from standard books on game theory (e.g., [3]) and stochastic learning [4]. In multiple automata games, instead of one automaton (player) playing against the environment, N automata, say A1, A2, ..., AN take part in a game. Consider a typical automaton Ai described by a 4-tuple {Si, ri, Ti,pi}. Each player i has a finite set of actions or pure strategies, Si, 1 ≤ i ≤ N . Let the cardinality of Si be mi, 1 ≤ i ≤ N . The result of each play is a random payoff to each player. Let ri denote the random payoff to player i, 1 ≤ i ≤ N . It is assumed here that ri ∈ [0, 1]. Define functions d : Π j=1Sj → [0, 1], 1 ≤ i ≤ N, by d(a1, ..., aN ) = E[ri|player j chose action aj , aj ∈ Sj , 1 ≤ j ≤ N ]. (0.1) The function d is called the expected payoff function or utility function of player i, 1 ≤ i ≤ N . The objective of each player is to maximize its expected payoff. Players choose their strategies based on a time-varying probability distribution. Let pi(k) = [pi1(k)...pimi(k)] t denote the action choice probability distribution of the i automaton at time instance k. Then pil(k) denotes the probability with which i automaton player chooses the l pure strategy at instant k. Thus pi(k) is the strategy probability vector employed by the i player at instant k. Ti denotes the stochastic learning algorithm according to which the elements of the set pi are updated at each time k, i.e.,

User strategy learning when pricing a RED buffer

We study a buffer that implements the Random Early Detect/Discard (RED) mechanism to cope with congestion, and offers service differentiation by proposing a finite number of slopes at different prices for the RED probability. As a characteristic, the smaller the slope, the better the resulting QoS. Users are sensitive to their average throughput and to the price they pay. Since the study of the noncooperative game played is rendered difficult by the discrete nature of the strategy sets, and since it is not likely that users have a perfect knowledge of the game but only know their experienced utility, we introduce a decentralized learning algorithm to progressively reach a Nash equilibrium over time. We examine the effect of prices on the final game outcomes.