Abstract The rapid proliferation of wireless systems makes interference management more and more important. This paper presents a novel cognitive coexistence framework, which enables an infrastructure system to reduce interference toad hocor peer‐to‐peer communication links in close proximity. Motivated by the superior resources of the infrastructure system, we study how its centralized resource allocation can accommodate thead hoclinks based on sensing and predicting their interference patterns. Based on an ON/OFF continuous‐time Markov chain model, the optimal allocation of power and transmission time is formulated as a convex optimization problem and structured solutions are derived. The optimal scheduling is extended to the case where the infrastructure channel is random and rate constraints need only be met in the long‐term average. Finally, the multi‐terminal case is addressed and the problem of optimal sub‐channel allocation is discussed. Numerical performance analysis illustrates that utilizing the superior flexibility of the infrastructure links can effectively mitigate interference. Copyright © 2009 John Wiley & Sons, Ltd.

This paper develops a novel fractional frequency reuse (FFR) technique to reduce co-channel interference in broadband cellular wireless networks. In contrast to methods proposed earlier, this technique relies on peer-to-peer connectivity among clients and is based on user cooperation: clients bargain for spectrum by exchanging channel quality metrics and jointly find the sub-channel allocation which maximizes throughput. Assuming full connectivity among clients, the spectral efficiency performance of client-centric FFR is studied analytically in conjunction with the use of opportunistic scheduling techniques that realize multiuser diversity gains with increasing network density and this serves as an upper bound on the achievable performance gain. The method is then extended to the clustered case, in which clients are partially-connected, a more typical situation in practice. Finally, a system-level study for an IEEE 802.16 based broadband cellular system in realistic propagation environments is conducted and demonstrates a promising performance gain.

The proliferation of wireless systems requires that the coexistence between heterogeneous technologies be addressed. This paper presents a cognitive framework in which sensing- based resource management of an infrastructure system effectively suppresses interference to close-by ad-hoc or peer-to- peer links. By utilizing its superior communication resources the infrastructure system estimates interference conditions and judiciously allocates transmission power such as to minimize interference. Despite adapting its transmission behavior, a rate constraint ensures that the infrastructure system continues to meet a specified quality-of-service level. The problem of optimal coexistence is formulated as a convex program. Structured solutions similar to classical water filling are derived and a solution method with guaranteed convergence is developed. An average-rate formulation extends the results to water filling across frequency and time. Numerical results corroborate our analysis and demonstrate a promising interference reduction.

We propose a randomized transmission scheme for minimizing a time-averaged cost metric in a cognitive radio. We assume that a single cognitive radio (i.e., a transmitter and receiver) hops over N orthogonal channels, each occupied by a primary user whose ON-OFF activity is modeled by a two-state Markov chain. We assume that the cognitive radio senses the activity in each channel at the beginning of every symbol period, and that a usage cost is assigned to each channel that depends on the channel’s physical-layer characteristics and the sensing outcome. We fully characterize the transmission scheme that minimizes the time-averaged cost, subject to interference constraints imposed by the primaries. Finally, we evaluate the performance for two special cases of the cost: the bit error rate and (lower and upper bounds on) the channel capacity.

In this paper we design a cognitive radio that can coexist with multiple parallel WLAN channels while abiding by an interference constraint. The interaction between both systems is characterized by measurement and coexistence is enhanced by predicting the WLAN's behavior based on a continuous-time Markov chain model. Cognitive medium access (CMA) is derived from this model by recasting the problem as one of constrained Markov decision processes. Solutions are obtained by linear programming. Furthermore, we show that optimal CMA admits structured solutions, simplifying practical implementations. Preliminary results for the partially observable case are presented. The performance of the proposed schemes is evaluated for a typical WLAN coexistence setup and shows a significant performance improvement.

In this paper we propose cognitive medium access (CMA), a protocol aimed at improving coexistence with a set of independently evolving WLAN bands. A time-slotted physical layer for the cognitive radio is considered and CMA is derived based on experimental models. By recasting the problem as a constrained Markov decision process (CMDP), throughput is optimized while keeping interference below some given constraint. The optimal control policy is obtained via linear programming. In addition, we show that optimal CMA admits structured solutions which are computationally less expensive and allow further insight into the problem. Numerical results are presented for typical coexistence setups and show a significant performance improvement.

The problem of dynamically accessing a set of parallel channels occupied by primary users is considered. The secondary user is allowed to sense and to transmit in a single channel. By exploiting idle periods between bursty transmissions of primary users, and by using a periodic sensing strategy, optimal dynamic access is achieved by maximizing the throughput of the secondary user while constraining collision probability with the primary user. The optimal dynamic spectrum access problem can then be formulated within the framework of constrained Markov decision processes (CMDPs). The optimal control policy is identified via a linear program, and its performance is analyzed numerically and through Monte Carlo simulations. Finally, we compare the optimal scheme to an ideal benchmark case when simultaneous sensing of all channels is assumed.

We consider the transmission of space-time block codes (STBC) over doubly-selective channels, which are encountered in highly mobile environments or high-frequency (HF) communications. In particular, we propose two extensions of time-reversal STBC with superior performance under these fast-fading conditions, and compare these schemes for various decision-feedback equalizers. The tradeoffs between performance and complexity are illustrated, and simulation results for the severely doubly-selective HF-channel corroborate our analysis

This letter examines the influence of low-bit quantization on moment estimators with special emphasis on the 1-bit case. Moment estimators are especially useful if no prior knowledge on the distribution of the observations is available or if an ML approach is analytically intractable or computationally infeasible. In order to arrive at analytical results for this very general case, we focus on a dithered quantization scheme that allows us to specify and analyze its asymptotic behavior. We show that consistency can be retained under mild conditions, and furthermore, we quantify the asymptotic variance. Additionally, we illustrate how to find an estimator that achieves the best performance possible in this scenario. Finally, we bolster our analytical results with simulations for the illustrative case of an AR(1) process and provide a comparison with undithered schemes. A conclusion summarizes this letter's contribution and explores possible areas of application