Stefan Geirhofer

Network densification: the dominant theme for wireless evolution into 5G

This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.

COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space

Dynamic spectrum access is a promising approach to alleviate the spectrum scarcity that wireless communications face today. In short, it aims at reusing sparsely occupied frequency bands while causing no (or insignificant) interference to the actual licensees. This article focuses on applying this concept in the time domain by exploiting idle periods between bursty transmissions of multi-access communication channels and addresses WLAN as an example of practical importance. A statistical model based on empirical data is presented, and it is shown how to use this model for deriving access strategies. The coexistence of Bluetooth and WLAN is considered as a concrete example

Opportunistic Spectrum Access via Periodic Channel Sensing

The problem of opportunistic access of parallel channels occupied by primary users is considered. Under a continuous-time 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.

Cognitive Medium Access: Constraining Interference Based on Experimental Models

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 WLANs 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.

A Measurement-Based Model for Dynamic Spectrum Access in WLAN Channels

In this paper we consider dynamically sharing the spectrum in the time-domain by exploiting whitespace between the bursty transmissions of a set of users, represented by an 802.11b based wireless LAN (WLAN). Realizing that exploiting the under-utilization of the channel requires a good model of the these users medium access, we propose a continuous-time semi-Markov model that captures the WLANs behavior yet remains tractable enough to be used for deriving optimal control strategies within a decision-theoretic framework. Our model is based on actual measurements in the 2.4 GHz ISM band using a vector signal analyzer to collect complex baseband data. We explore two different sensing strategies to identify spectrum opportunities depending on whether the primary users transmission scheme is known. The collected data is used to statistically characterize the idle and busy periods of the channel. Furthermore, we show that a continuous-time semi-Markov model is able to capture the data with good accuracy. The Kolmogorov-Smirnov test is used to validate the model and to assess the models goodness-of-fit quantitatively. A conclusion summarizes the main results of the paper

Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis

In this work we are concerned with dynamically sharing the spectrum in the time-domain by exploiting whitespace between the bursty transmissions of a primary user, represented by an 802.11b-based wireless LAN (WLAN). For deriving such schemes we need to establish a model of the WLANs medium access as to predict its behavior accurately. Moreover, a balance between accuracy and complexity needs to be struck as to render the model useful in practice. We emphasize that our model is based on actual measurements at 2.4GHz using a vector signal analyzer.n We have shown previously that a semi-Markov model is a viable approach for modeling the busy/idle durations. In the present paper we extend our results by (i) expanding the measurement setup and looking at more realistic traffic scenarios, (ii) providing a better approximation to the distribution of the idle durations, and (iii) fitting a phasetype approximation to arrive at a computationally simpler description. The goodness-of-fit of the proposed models is evaluated using the Kolmogorov-Smirnov test.

Optimal Dynamic Spectrum Access via Periodic Channel Sensing

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.

Cognitive frequency hopping based on interference prediction: theory and experimental results

Wireless services in the unlicensed bands are proliferating but frequently face high interference from other devices due to a lack of coordination among heterogeneous technologies. In this paper we study how cognitive radio concepts enable systems to sense and predict interference patterns and adapt their spectrum access accordingly. This leads to a new cognitive coexistence paradigm, in which cognitive radio implicitly coordinates the spectrum access of heterogeneous systems. Within this framework, we investigate coexistence with a set of parallel WLAN bands: based on predicting WLAN activity, the cognitive radio dynamically hops between the bands to avoid collisions and reduce interference. The development of a real-time test bed is presented, and used to corroborate theoretical results and model assumptions. Numerical results show a good fit between theory and experiment and demonstrate that sensing and prediction can mitigate interference effectively.

Cognitive Medium Access: A Protocol for Enhancing Coexistence in WLAN Bands

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.

Interference-aware OFDMA resource allocation: A predictive approach

As wireless systems continue to proliferate, interference management is becoming a concern in both military and commercial domains. This paper introduces a novel cognitive coexistence framework between infrastructure and ad hoc networks. Based on sensing and predicting the ad-hoc networkpsilas activity, the infrastructure system allocates power and transmission time such as to minimize its impact on the ad-hoc links. This leads to an interference-aware resource allocation. A rate-constraint ensures that the infrastructure system maintains a specified quality-of-service, despite adapting its transmission behavior to accommodate ad-hoc users. 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. Closed-form solutions are derived as a function of Lagrange multipliers. An iterative algorithm with guaranteed convergence to the optimal solution is developed. Finally, our results are extended to an average-rate formulation. Numerical performance analysis illustrates that utilizing the superior flexibility of the infrastructure links can effectively mitigate interference.