Sudharman K. Jayaweera

Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks

An energy-efficient virtual multiple-input multiple-output (MIMO)-based communication architecture is proposed for distributed and cooperative wireless sensor networks. Assuming a space-time block coding (STBC) based MIMO system, the energy and delay efficiencies of the proposed scheme are derived using semi-analytic techniques. The dependence of these efficiency values on physical channel propagation parameters, fading coherence time and the amount of required training is also investigated. The results show that with judicious choice of design parameters the virtual MIMO technique can be made to provide significant energy and delay efficiencies, even after allowing for additional training overheads.

Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games

Hierarchical dynamic spectrum access (DSA) has received the most attention in recent years as the solution for better spectrum utilization. In this paper, on the other hand, we develop a framework for dynamic spectrum leasing (DSL). Power control in hierarchical DSA networks only involves that of controlling secondary user transmissions. Thus, in game theoretic formulations of power control in cognitive DSA networks only secondary users are considered as players of the game. In proposed dynamic spectrum leasing, on the other hand, the primary users are rewarded for allowing secondary users to operate in their licensed spectrum. Thus, in the proposed DSL networks the primary users have an incentive to allow secondary users to access the spectrum whenever possible to the maximum extent. We develop a game theoretic framework for such dynamic spectrum leasing in which primary users actively participate in a non-cooperative game with secondary users by selecting an interference cap on the total interference they willing to tolerate. We establish that the proposed primary-secondary user power control game has a unique Nash equilibrium. Performance of a DSL system based on the proposed game model is compared through simulations under different linear receivers at the secondary base station.

A Survey on Machine-Learning Techniques in Cognitive Radios

In this survey paper, we characterize the learning problem in cognitive radios (CRs) and state the importance of artificial intelligence in achieving real cognitive communications systems. We review various learning problems that have been studied in the context of CRs classifying them under two main categories: Decision-making and feature classification. Decision-making is responsible for determining policies and decision rules for CRs while feature classification permits identifying and classifying different observation models. The learning algorithms encountered are categorized as either supervised or unsupervised algorithms. We describe in detail several challenging learning issues that arise in cognitive radio networks (CRNs), in particular in non-Markovian environments and decentralized networks, and present possible solution methods to address them. We discuss similarities and differences among the presented algorithms and identify the conditions under which each of the techniques may be applied.

Capacity of multiple-antenna systems with both receiver and transmitter channel state information

The capacity of multiple-antenna systems operating in Rayleigh flat fading is considered under the assumptions that channel state information (CSI) is available at both transmitter and receiver, and that the transmitter is subjected to an average power constraint. First, the capacity of such systems is derived for the special case of multiple transmit antennas and a single receive antenna. The optimal power-allocation scheme for such a system is shown to be a water-filling algorithm, and the corresponding capacity is seen to be the same as that of a system having multiple receive antennas (with a single transmitter antenna) whose outputs are combined via maximal ratio combining. A suboptimal adaptive transmission technique that transmits only over the antenna having the best channel is also proposed for this special case. It is shown that the capacity of such a system under the proposed suboptimal adaptive transmission scheme is the same as the capacity of a system having multiple receiver antennas (with a single transmitter antenna) combined via selection combining. Next, the capacity of a general system of multiple transmitter and receiver antennas is derived together with an equation that determines the cutoff value for such a system. The optimal power allocation scheme for such a multiple-antenna system is given by a matrix water-filling algorithm. In order to eliminate the need for cumbersome numerical techniques in solving the cutoff equation, approximate expressions for the cutoff transmission value are also provided. It is shown that, compared to the case in which there is only receiver CSI, large capacity gains are available with optimal power and rate adaptation schemes. The increased capacity is shown to come at the price of channel outage, and bounds are derived for this outage probability.

Dynamic Spectrum Leasing: A New Paradigm for Spectrum Sharing in Cognitive Radio Networks

We recently proposed the dynamic spectrum leasing (DSL) paradigm for dynamic spectrum access in cognitive radio networks. In this paper, we formalize this concept by developing a general game-theoretic framework for the DSL and by carefully identifying requirements for the coexistence of primary and secondary systems via spectrum leasing. In contrast to hierarchical spectrum access, spectrum owners in proposed DSL networks, which are denoted as primary users, dynamically adjust the amount of secondary interference that they are willing to tolerate in response to the demand from secondary transmitters. The secondary transmitters, in turn, opportunistically attempt to achieve maximum possible throughput, or another suitably defined reward, while not violating the interference limit that is set by the primary users. The new game-theoretic model, however, allows the secondary users to encourage the spectrum owners to push the interference cap upward based on demand. We have proposed a general structure for the utility functions of primary users and secondary users that allows the primary users to control the price and the demand for spectrum access based on their required quality of service (QoS). We establish that, with these utility functions, the DSL game has a unique Nash equilibrium to which the best response adaptation finally converges. Moreover, it is shown that the proposed coexistence and best response adaptations can be achieved with no significant interaction between the two systems. In fact, it is shown that the only requirement is that the primary system periodically broadcasts two parameter values. We use several examples to illustrate the system behavior at the equilibrium and use the performance at the equilibrium to identify suitable system design parameters.

On the capacity of multiple-antenna systems in Rician fading

The effect of Rician-ness on the capacity of multiple-antenna systems is investigated under the assumption that channel state information (CSI) is available only at the receiver. The average-power-constrained capacity of such systems is considered under two different assumptions on the available knowledge about fading at the transmitter: the case in which the transmitter has no knowledge of fading at all, and the case in which the transmitter has knowledge of the distribution of the fading process but not the instantaneous CSI. The exact capacity is given for the former case, while capacity bounds are derived for the latter case. A new signaling scheme is also proposed for the latter case and it is shown that by exploiting the knowledge of Rician-ness at the transmitter via this signaling scheme, significant capacity gain can be achieved. The derived capacity bounds are evaluated explicitly to provide numerical results in some representative situations.

Asymmetric Cooperative Communications Based Spectrum Leasing via Auctions in Cognitive Radio Networks

Dynamic spectrum leasing (DSL) was proposed recently as a new paradigm for dynamic spectrum sharing (DSS) in cognitive radio networks (CRNs). In this paper, we propose a new way to encourage primary users to lease their spectrum: The secondary users (SUs) place bids indicating how much power they are willing to spend for relaying the primary signals to their destinations. In this formulation, the primary users achieve power savings due to asymmetric cooperation. We propose and analyze both a centralized and a distributed decision-making architecture for the secondary CRN. In the centralized architecture, a Secondary System Decision Center (SSDC) selects a bid for each primary channel based on optimal channel assignment for SUs. In the decentralized cognitive network architecture, we formulate an auction game-based protocol in which each SU independently places bids for each primary channel and receivers of each primary link pick the bid that will lead to the most power savings. A simple and robust distributed reinforcement learning mechanism is developed to allow the users to revise their bids and to increase their rewards. The performance results show the significant impact of reinforcement learning in both improving spectrum utilization and meeting individual SU performance requirements.

An energy-efficient virtual MIMO architecture based on V-BLAST processing for distributed wireless sensor networks

An energy-efficient virtual multiple-input multiple-output (MIMO) communication architecture based on V-BLAST receiver processing is proposed for energy-constrained, distributed wireless sensor networks. The proposed scheme does not require transmitter-side sensor cooperation unlike previously proposed virtual MlMO schemes for wireless sensor networks. In sensor networks with single-antenna data gathering nodes, the virtual MIMO operation is realized via the receiver-side local communication assuming node cooperation. Numerical results show that the significant energy savings are offered by the proposed virtual MIMO architecture in distributed wireless sensor networks. These results also indicate that while rate optimization over transmission distance may offer improved energy efficiencies in some cases, this is not essential in achieving energy savings as opposed to previously proposed Alamouti scheme-based virtual MIMO implementations. In fact, in most scenarios a fixed-rate virtual MIMO system with binary phase-shift-keying (BPSK) can achieve performance very close to that of a variable-rate system with optimized rates. However, these results also indicate that the proposed scheme can lead to larger delay penalties compared to a traditional SISO communication based sensor network as the order of the virtual MIMO architecture grows. This results in a trade-off between the achievable energy efficiency and the delay incurred, making the proposed virtual V-BLAST based MIMO scheme an especially good candidate communication architecture for energy-starved and delay-tolerant wireless sensor networks having no inter-sensor communication.

Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO

Optimal power scheduling for distributed detection in a Gaussian sensor network is addressed for both independent and correlated observations. We assume amplify-and-forward local processing at each node. The wireless link between sensors and the fusion center is assumed to undergo fading and coefficients are assumed to be available at the transmitting sensors. The objective is to minimize the total network power to achieve a desired fusion error probability at the fusion center. For i.i.d. observations, the optimal power allocation is derived analytically in closed form. When observations are correlated, first, an easy to optimize upper bound is derived for sufficiently small correlations and the power allocation scheme is derived accordingly. Next, an evolutionary computation technique based on particle swarm optimization is developed to find the optimal power allocation for arbitrary correlations. The optimal power scheduling scheme suggests that the sensors with poor observation quality and bad channels should be inactive to save the total power expenditure of the system. It is shown that the probability of fusion error performance based on the optimal power allocation scheme outperforms the uniform power allocation scheme especially when either the number of sensors is large or the local observation quality is good.

V-BLAST-Based Virtual MIMO for Distributed Wireless Sensor Networks

A virtual multiple-input multiple-output (MIMO) communications architecture based on vertical Bell Laboratories layered space-time (V-BLAST) receiver processing is proposed for wireless sensor networks (WSNs). The proposed scheme does not require transmitter-side node cooperation unlike previously proposed virtual MIMO schemes. The energy and delay efficiencies of the proposed virtual MIMO scheme are derived for networks with both single-and multiple-antenna data gathering nodes (DGNs). Numerical results show the significant energy savings offered by the proposed method. These results also indicate that rate optimization over transmission distance is not essential as in virtual MIMO systems based on Alamouti scheme. In most scenarios, a fixed-rate virtual MIMO system with 4-quadrature amplitude modulation can achieve performance very close to that of an optimized, variable-rate system. In the case of single-antenna DGNs, the proposed scheme typically incur larger delay values compared to traditional single-input single-output communication, making it a good candidate for energy-starved but delay-tolerant WSNs.