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121
Joint design and separation principle for opportunistic spectrum access
- IEEE Transactions on Information Theory
, 2006
"... Abstract — This paper develops optimal strategy for opportunistic spectrum access (OSA) by integrating the design of spectrum sensor at the physical layer with that of spectrum sensing and access policies at the medium access control (MAC) layer. The design objective is to maximize the throughput of ..."
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Cited by 137 (35 self)
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Abstract — This paper develops optimal strategy for opportunistic spectrum access (OSA) by integrating the design of spectrum sensor at the physical layer with that of spectrum sensing and access policies at the medium access control (MAC) layer. The design objective is to maximize the throughput of secondary users while limiting their probability of colliding with primary users. By exploiting the rich structures of the problem, we establish a separation principle: the design of spectrum sensor and access policy can be decoupled from that of sensing policy without losing optimality. This separation principle enables us to obtain closedform optimal sensor operating characteristic and access policy, leading to significant complexity reduction. It also allows us to study the inherent interaction between spectrum sensor and access policy and the tradeoff between false alarm and miss detection in opportunity identification. I.
Competitive pricing for spectrum sharing in cognitive radio networks: Dynamic game, inefficiency of nash equilibrium, and collusion
- IEEE JSAC
"... Abstract—We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maxim ..."
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Cited by 94 (0 self)
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Abstract—We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future. Index Terms—Spectrum sharing, cognitive radio, pricing scheme, game theory, Nash equilibrium, distributed adaptation, collusion. I.
Opportunistic Spectrum Access via Periodic Channel Sensing
- IEEE Trans. Sig. Proc
, 2008
"... Abstract—The problem of opportunistic access of parallel channels occupied by primary users is considered. Under a continuoustime Markov chain modeling of the channel occupancy by the primary users, a slotted transmission protocol for secondary users using a periodic sensing strategy with optimal dy ..."
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Cited by 81 (14 self)
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Abstract—The problem of opportunistic access of parallel channels occupied by primary users is considered. Under a continuoustime Markov chain modeling of the channel occupancy by the primary users, a slotted transmission protocol for secondary users using a periodic sensing strategy with optimal dynamic access is proposed. To maximize channel utilization while limiting interference to primary users, a framework of constrained Markov decision processes is presented, and the optimal access policy is derived via a linear program. Simulations are used for performance evaluation. It is demonstrated that periodic sensing yields negligible loss of throughput when the constraint on interference is tight. Index Terms—Constrained Markov decision processes, dynamic spectrum access, resource allocation. I.
Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity
"... Abstract—A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands ..."
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Cited by 80 (7 self)
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Abstract—A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands at arbitrary locations, and thus facilitates spatial frequency reuse. The novel scheme capitalizes on two forms of sparsity: the first one introduced by the narrow-band nature of transmit-PSDs relative to the broad swaths of usable spectrum; and the second one emerging from sparsely located active radios in the operational space. An estimator of the model coefficients is developed based on the Lasso algorithm to exploit these forms of sparsity and reveal the unknown positions of transmitting CRs. The resultant scheme can be implemented via distributed online iterations, which solve quadratic programs locally (one per radio), and are adaptive to changes in the system. Simulations corroborate that exploiting sparsity in CR sensing reduces spatial and frequency spectrum leakage by 15 dB relative to least-squares (LS) alternatives. Index Terms—Cognitive radios, compressive sampling, cooperative systems, distributed estimation, parallel network processing, sensing, sparse models, spectral analysis. I.
Opportunistic scheduling with reliability guarantees in cognitive radio networks
- In Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved. International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012 ISSN (Online): 1694-0814 www.IJCSI.org 73 INFOCOM 2008. The 27th Conference on
, 2008
"... Abstract—We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potent ..."
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Cited by 79 (8 self)
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Abstract—We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potentially mobile secondary users. We use the technique of Lyapunov Optimization to design an online flow control, scheduling and resource allocation algorithm that meets the desired objectives and provides explicit performance guarantees.
A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions
, 2010
"... Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area ..."
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Cited by 60 (9 self)
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Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.
Optimal resource allocation for MIMO ad hoc cognitive radio networks
- in Proc. 46th Annu. Allerton Conf. Commun., Control, Comput
, 2008
"... Abstract—Maximization of the weighted sum-rate of secondary users (SUs) possibly equipped with multiantenna transmitters and receivers is considered in the context of cognitive radio (CR) net-works with coexisting primary users (PUs). The total interference power received at the primary receiver is ..."
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Cited by 39 (0 self)
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Abstract—Maximization of the weighted sum-rate of secondary users (SUs) possibly equipped with multiantenna transmitters and receivers is considered in the context of cognitive radio (CR) net-works with coexisting primary users (PUs). The total interference power received at the primary receiver is constrained to main-tain reliable communication for the PU. An interference channel configuration is considered for ad hoc networking, where the re-ceivers treat the interference from undesired transmitters as noise. Without the CR constraint, a convergent distributed algorithm is developed to obtain (at least) a locally optimal solution. With the CR constraint, a semidistributed algorithm is introduced. An al-ternative centralized algorithm based on geometric programming and network duality is also developed. Numerical results show the efficacy of the proposed algorithms. The novel approach is flexible to accommodate modifications aiming at interference alignment. However, the stand-alone weighted sum-rate optimal schemes pro-posed here have merits over interference-alignment alternatives es-pecially for practical SNR values. Index Terms—Ad hoc network, cognitive radio, interference net-work, MIMO, optimization. I.
Learning Multiuser Channel Allocations in Cognitive Radio Networks: A Combinatorial Multi-Armed Bandit Formulation
"... Abstract—We consider the following fundamental problem in the context of channelized dynamic spectrum access. There are M secondary users and N ≥ M orthogonal channels. Each secondary user requires a single channel for operation that does not conflict with the channels assigned to the other users. D ..."
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Cited by 39 (6 self)
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Abstract—We consider the following fundamental problem in the context of channelized dynamic spectrum access. There are M secondary users and N ≥ M orthogonal channels. Each secondary user requires a single channel for operation that does not conflict with the channels assigned to the other users. Due to geographic dispersion, each secondary user can potentially see different primary user occupancy behavior on each channel. Time is divided into discrete decision rounds. The throughput obtainable from spectrum opportunities on each userchannel combination over a decision period is modeled as an arbitrarily-distributed random variable with bounded support but unknown mean, i.i.d. over time. The objective is to search for an allocation of channels for all users that maximizes the expected sum throughput. We formulate this problem as a combinatorial multi-armed bandit (MAB), in which each arm corresponds to a matching of the users to channels. Unlike most prior work on multi-armed bandits, this combinatorial formulation results in dependent arms. Moreover, the number of arms grows super-exponentially as the permutation P (N, M). We present a novel matching-learning algorithm with polynomial storage and polynomial computation per decision period for this problem, and prove that it results in a regret (the gap between the expected sum-throughput obtained by a genie-aided perfect allocation and that obtained by this algorithm) that is uniformly upper-bounded for all time n by a function that grows as O(M 4 Nlogn), i.e. polynomial in the number of unknown parameters and logarithmic in time. We also discuss how our results provide a non-trivial generalization of known theoretical results on multi-armed bandits. I.
Throughput-efficient Sequential Channel Sensing and Probing in Cognitive Radio Networks Under Sensing Errors
- In Proc. ACM MobiCom
, 2009
"... In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic trans-mission. The difficulty of the problem comes from the fac ..."
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Cited by 38 (2 self)
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In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic trans-mission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all chan-nels and then pick the best among them, due to the poten-tially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid colli-sions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR’s average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that ad-dresses all the above considerations is derived by formulat-ing the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to con-duct a “second-round ” optimization over the operational pa-rameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone.
Spectrum trading in cognitive radio networks: a contract-theoretic modeling approach
- IEEE J. Selected Areas Commun
, 2011
"... Abstract—Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unli-censed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In ..."
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Cited by 28 (5 self)
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Abstract—Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unli-censed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.
