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35
Competitive design of multiuser MIMO systems based on game theory: A unified view
 IEEE Journal on Selected Areas in Communications
, 2008
"... Abstract—This paper considers the noncooperative maximization of mutual information in the Gaussian interference channel in a fully distributed fashion via game theory. This problem has been studied in a number of papers during the past decade for the case of frequencyselective channels. A variety ..."
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Cited by 29 (2 self)
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Abstract—This paper considers the noncooperative maximization of mutual information in the Gaussian interference channel in a fully distributed fashion via game theory. This problem has been studied in a number of papers during the past decade for the case of frequencyselective channels. A variety of conditions guaranteeing the uniqueness of the Nash Equilibrium (NE) and convergence of many different distributed algorithms have been derived. In this paper we provide a unified view of the stateoftheart results, showing that most of the techniques proposed in the literature to study the game, even though apparently different, can be unified using our recent interpretation of the waterfilling operator as a projection onto a proper polyhedral set. Based on this interpretation, we then provide a mathematical framework, useful to derive a unified set of sufficient conditions guaranteeing the uniqueness of the NE and the global convergence of waterfilling based asynchronous distributed algorithms. The proposed mathematical framework is also instrumental to study the extension of the game to the more general MIMO case, for which only few results are available in the current literature. The resulting algorithm is, similarly to the frequencyselective case, an iterative asynchronous MIMO waterfilling algorithm. The proof of convergence hinges again on the interpretation of the MIMO waterfilling as a matrix projection, which is the natural generalization of our results obtained for the waterfilling mapping in the frequencyselective case. Index Terms—Game Theory, MIMO Gaussian interference channel, Nash equilibrium, totally asynchronous algorithms, waterfilling. I.
1 Dynamic Spectrum Management with the Competitive Market Model
"... Abstract — [1], [2] have shown that dynamic spectrum management (DSM) using the market competitive equilibrium (CE), which sets a price for transmission power on each channel, leads to better system performance in terms of the total data transmission rate (by reducing cross talk), than using the Nas ..."
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Cited by 6 (0 self)
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Abstract — [1], [2] have shown that dynamic spectrum management (DSM) using the market competitive equilibrium (CE), which sets a price for transmission power on each channel, leads to better system performance in terms of the total data transmission rate (by reducing cross talk), than using the Nash equilibrium (NE). But how to achieve such a CE is an open problem. We show that the CE is the solution of a linear complementarity problem (LCP) and can be computed efficiently. We propose a decentralized tâtonnement process for adjusting the prices to achieve a CE. We show that under reasonable conditions, any tâtonnement process converges to the CE. The conditions are that users of a channel experience the same noise levels and that the crosstalk effects between users are lowrank and weak. Index Terms—Radio spectrum management, dynamic spectrum management (DSM), linear complementarity problem (LCP), competitive equilibrium I.
Spectrum sharing games on the interference channel
 in Proc. IEEE Intl. Conf. on Game Theory for Networks (Gamenets
, 2010
"... Abstract—In this paper, we address the problem of spectrum sharing where competitive operators coexist in the same frequency band. First, we model this problem as a strategic noncooperative game where operators simultaneously share the spectrum according to the Nash Equilibrium (N.E). Given a set o ..."
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Cited by 4 (2 self)
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Abstract—In this paper, we address the problem of spectrum sharing where competitive operators coexist in the same frequency band. First, we model this problem as a strategic noncooperative game where operators simultaneously share the spectrum according to the Nash Equilibrium (N.E). Given a set of channel realizations, several Nash equilibria exist which renders the outcome of the game unpredictable. For this reason, the spectrum sharing problem is reformulated as a Stackelberg game where the first operator is already being deployed and the secondary operator follows next. The Stackelberg equilibrium (S.E) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator’s utility function. Finally, we assess the goodness of the proposed distributed approach by comparing its performance to the centralized approach. I.
A Generalized Iterative Waterfilling Algorithm for Distributed Power Control in the Presence
, 2008
"... Consider a scenario in which K users and a jammer share a common spectrum of N orthogonal tones. Both the users and the jammer have limited power budgets. The goal of each user is to allocate its power across the N tones in such a way that maximizes the total sum rate that he/she can achieve, while ..."
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Cited by 3 (1 self)
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Consider a scenario in which K users and a jammer share a common spectrum of N orthogonal tones. Both the users and the jammer have limited power budgets. The goal of each user is to allocate its power across the N tones in such a way that maximizes the total sum rate that he/she can achieve, while treating the interference of other users and the jammer’s signal as additive Gaussian noise. The jammer, on the other hand, wishes to allocate its power in such a way that minimizes the utility of the whole system; that being the total sum of the rates communicated over the network. For this noncooperative game, we propose a generalized version of the existing iterative waterfilling algorithm whereby the users and the jammer update their power allocations in a greedy manner. We study the existence of a Nash equilibrium of this noncooperative game as well as conditions under which the generalized iterative waterfilling algorithm converges to a Nash equilibrium of the game. The conditions that we derive in this paper depend only on the system parameters, and hence can be checked a priori. Simulations show that when the convergence conditions are violated, the presence of a jammer can cause the, otherwise convergent, iterative waterfilling algorithm to oscillate.
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 sumrate of secondary users (SUs) possibly equipped with multiantenna transmitters and receivers is considered in the context of cognitive radio (CR) networks with coexisting primary users (PUs). The total interference power received at the primary receiver is ..."
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Cited by 2 (0 self)
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Abstract—Maximization of the weighted sumrate of secondary users (SUs) possibly equipped with multiantenna transmitters and receivers is considered in the context of cognitive radio (CR) networks with coexisting primary users (PUs). The total interference power received at the primary receiver is constrained to maintain reliable communication for the PU. An interference channel configuration is considered for ad hoc networking, where the receivers 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 alternative 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 standalone weighted sumrate optimal schemes proposed here have merits over interferencealignment alternatives especially for practical SNR values. Index Terms—Ad hoc network, cognitive radio, interference network, MIMO, optimization. I.
1 Equilibrium Pricing of Interference in Cognitive Radio Networks
"... In this paper we address the problem of spectrum allocation in cognitive radio networks in which licensed users allow unlicensed users to make use of their allocated capacity, provided a set of interference constraints on the receivers of licensed users are satisfied. In this scenario, the tradition ..."
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Cited by 2 (2 self)
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In this paper we address the problem of spectrum allocation in cognitive radio networks in which licensed users allow unlicensed users to make use of their allocated capacity, provided a set of interference constraints on the receivers of licensed users are satisfied. In this scenario, the traditional distributed dynamical spectrum allocation approach is unable to enforce such interference constraints. To address this problem, we propose to allocate the spectrum to unlicensed users by pricing the interference constraints. In such a scheme, a total charge for each unlicensed user will be determined based upon their contribution to the total interference as measured by each licensed user. We also formulate the notion of interference equilibrium, which is a state of the network where i) all interference constraints are satisfied and ii) no unlicensed user has an incentive to alter its own transmission power levels. We propose a distributed algorithm and apply it to two cognitive radio network configurations. For each configuration we give sufficient conditions to guarantee convergence of the unlicensed users ’ power profiles and ensure interference prices converge to an interference equilibrium. I.
LEARNING DISTRIBUTED POWER ALLOCATION POLICIES IN MIMO CHANNELS
"... In this paper 1, we study the discrete power allocation game for the fast fading multipleinput multipleoutput multiple access channel. Each player or transmitter chooses its own transmit power policy from a certain finite set to optimize its individual transmission rate. First, we prove the existe ..."
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Cited by 1 (1 self)
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In this paper 1, we study the discrete power allocation game for the fast fading multipleinput multipleoutput multiple access channel. Each player or transmitter chooses its own transmit power policy from a certain finite set to optimize its individual transmission rate. First, we prove the existence of at least one pure strategy Nash equilibrium. Then, we investigate two learning algorithms that allow the players to converge to either one of the NE states or to the set of correlated equilibria. At last, we compare the performance of the considered discrete game with the continuous game in [7]. 1.
JOINT DYNAMIC RESOURCE ALLOCATION AND WAVEFORM ADAPTATION IN COGNITIVE RADIO NETWORKS
"... This paper discusses the issue of dynamic resource allocation (DRA) in the context of cognitive radio (CR) networks. We present a general framework adopting generalized transmitter and receiver signalexpansion functions, which allow us to join DRA with waveform adaptation, two procedures that are cu ..."
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Cited by 1 (0 self)
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This paper discusses the issue of dynamic resource allocation (DRA) in the context of cognitive radio (CR) networks. We present a general framework adopting generalized transmitter and receiver signalexpansion functions, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Moreover, the proposed DRA can handle many types of expansion functions or even combinations of different types of functions. An iterative game approach is adopted to perform multiplayer DRA, and the bestresponse strategies of players are derived and characterized using convex optimization. To reduce the implementation costs of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints for dynamic function selection. Generally, it incurs little rateperformance loss since the effective resources required by a CR are in fact sparse. Index Terms — cognitive radio, dynamic resource allocation, game theory, waveform adaptation, sparsity 1.