This paper considers downlink spectrum allocation in a long term evolution (LTE) system macrocell which contains multiple femtocells. By incorporating cognitive capabilities into femtocell base stations, the Home evolved Node Bs (HeNBs) can be formulated as secondary base stations seeking to maximize the spectrum utility while minimizing interference to primary base stations (evolved Node-Bs). The competition amongst cognitive HeNBs for spectrum resources is formulated as a non-cooperative game-theoretic learning problem where each agent (HeNB) seeks to adapt its strategy in real time. We formulate the resource block (RB) allocation among HeNBs in the downlink of a LTE system using a game-theoretic framework, where the correlated equilibrium solutions of the formulated game are being investigated. A distributed RB access algorithm is proposed to compute the correlated equilibrium RB allocation policy..

This paper considers a Markovian dynamical game theoretic setting for distributed transmission control in a wireless sensor network. The available spectrum bandwidth is modeled as a Markov chain. A distributed algorithm named correlated Q-learning algorithm is proposed to obtain the correlated equilibrium policies of the system. This algorithm has the decentralized feature and is easily implementable in a real system. Numerical example is also provided to verify the performances of the proposed algorithms.

This paper considers an uplink time division multiple access (TDMA) cognitive radio network where multiple cognitive radios (secondary users) attempt to access a spectrum hole. We assume that each secondary user can access the channel according to a decentralized predefined access rule based on the channel quality and the transmission delay of each secondary user. By modeling secondary user block fading channel qualities as a finite state Markov chain, we formulate the transmission rate adaptation problem of each secondary user as a general-sum Markovian dynamic game with a delay constraint. Conditions are given so that the Nash equilibrium transmission policy of each secondary user is a randomized mixture of pure threshold policies. Such threshold policies can be easily implemented. We then present a stochastic approximation algorithm that can adaptively estimate the Nash equilibrium policies and track such policies for non-stationary problems where the statistics of the channel and user parameters evolve with time.

This paper addresses the secondary user rate adaptation problem in cognitive radio networks. By modeling primary user activities and secondary user block fading channels as finite state Markov chains, the transmission rate adaptation problem of each secondary user is formulated as a general-sum dynamic Markovian game with a delay constraint. Assumptions are given so that the Nash equilibrium transmission policy of each user is a randomized mixture of pure threshold policies. We also present a stochastic approximation algorithm which can adaptively estimate the Nash equilibrium policies and track such policies for non-stationary problems where the statistics of the channel and user parameters evolve with time.

We propose a joint set of linear precoder designs for single cell uplink multiuser space-time block coded multiple- input multiple-output systems with multi-packet reception by exploiting outdated channel state information. By deriving the pairwise error probability with respect to both minimum and average codeword distance design metrics, we formulate the design as an optimization problem subject to transmit power constraint for each user. Due to the non-convex nature of the optimization problem, we devise an iterative algorithm to solve for linear precoding structure for general space-time block code. For orthogonal space-time block code, we also propose a simplified distributed algorithm to solve for a closed-form solution. Asymptotic analysis on the effect of quality of the outdated channel state information (CSI) on the precoder structure is also presented. Simulation results are provided to demonstrate the effectiveness of the proposed designs for different space-time block codes at various CSI qualities.

We address the problem of designing a linear precoder for space-time coded multiple-input multiple-output (MIMO) system with imperfect knowledge of channel state information at the transmitter (CSIT), subject to a total transmit power constraint. Assuming an uncorrelated flat-fading channel and using a maximum likelihood decoder at the receiver, we analytically reveal that the derived precoder is a function of the noise variance, the eigenvalues of the estimated channel matrix and the eigenvalues of the codeword distance matrix. Furthermore, the power allocation on the eigenvalues of the precoder is shown to follow the water-pouring policy and hence the proposed precoder allocates more power to the stronger channels.

Motivated by the fact that combining space-time block codes with channel precoding can make the system robust against channel fading while achieving both coding and diversity gains, we propose a joint set of linear precoder designs for uplink multiuser space-time block coded multiple-input multiple-output systems with multipacket reception by exploiting outdated channel state information. In particular, we formulate the design as an optimization problem of minimizing pairwise error probability subject to transmit power constraint for each user. Due to non-convex nature of the optimization problem, we devise an iterative algorithm to solve for linear precoding structure for general space-time block code. For orthogonal space-time block code, we also propose a simplified distributed algorithm to solve for a closed-form solution. Asymptotic analysis of the effect of channel quality on the precoder structure is also presented. Simulation results are provided to demonstrate the effectiveness of the proposed designs for orthogonal and quasi-orthogonal space-time block codes over the conventional beamforming system.

This paper considers a joint design of linear precoders and equalizers for uplink multiuser multiple-input multiple-output communications systems over uncorrelated flat-fading channels with imperfect channel state information at both the transmitters and the receivers. By minimizing the mean square error (MSE) of all users, or equivalently the sum-MSE, under a sum-power constraint, we model the linear transceiver design as an optimization problem. Instead of relying on complex iterative algorithms, the authors propose a low-complexity, non-iterative but close-to-optimal precoder design solution by relaxing the problem into a simpler convex optimization problem. In this way, a closed-form linear transceiver structure can be derived by using the Karush-Kuhn-Tucker (KKT) optimality conditions. Based on the closed-form solution and random matrix theory, the authors obtain a simple asymptotic expression on the average sum-MSE, which can provide useful design insights.

We address the problem of designing a linear precoder for space-time coded multiple-input multiple-output (MIMO) system with partial knowledge of channel state information at the transmitter (CSIT), subject to a total transmit power constraint. Assuming an uncorrelated flat-fading channel and using a maximum-likelihood detector at the receiver, we show that the derived precoder is a function of the noise variance, the eigenvalues of the estimated channel matrix and the eigenvalues of the codeword error matrix. From the results derived herein, it is observed that the power allocation on the eigenvalues of the precoder follows the water-pouring policy. Simulation results are provided to illustrate the significant performance gain achieved by using the proposed precoder even at moderate to large CSIT errors.