Rahul Tandra

What is a Spectrum Hole and What Does it Take to Recognize One

ldquoSpectrum holesrdquo represent the potential opportunities for noninterfering (safe) use of spectrum and can be considered as multidimensional regions within frequency, time, and space. The main challenge for secondary radio systems is to be able to robustly sense when they are within such a spectrum hole. To allow a unified discussion of the core issues in spectrum sensing, the ldquoweighted probability of area recoveredrdquo (WPAR) metric is introduced to measure the performance of a sensing strategy; and the ldquofear of harmful interferencerdquo F HI metric is introduced to measure its safety. These metrics explicitly consider the impact of asymmetric uncertainties (and misaligned incentives) in the system model. Furthermore, they allow a meaningful comparison of diverse approaches to spectrum sensing unlike the traditional triad of sensitivity, probability of false-alarm P FA, and probability of missed-detection P MD. These new metrics are used to show that fading uncertainty forces the WPAR performance of single-radio sensing algorithms to be very low for small values of F HI, even for ideal detectors. Cooperative sensing algorithms enable a much higher WPAR, but only if users are guaranteed to experience independent fading. Lastly, in-the-field calibration for wide-band (but uncertain) environment variables (e.g., interference and shadowing) can robustly guarantee safety (low F HI) even in the face of potentially correlated users without sacrificing WPAR.

Fundamental design tradeoffs in cognitive radio systems

Under the current system of spectrum allocation, rigid partitioning has resulted in vastly underutilized spectrum bands, even in urban locales. Cognitive radios have been proposed as a way to reuse this underutilized spectrum in an opportunistic manner. To achieve this reuse while guaranteeing non-interference with the primary user, cognitive radios must detect very weak primary signals. However, uncertainties in the noise+interference impose a limit on how low of a primary signal can be robustly detected. In this paper, we show that the presence/absence of possible interference from other opportunistic spectrum users represents a major component of the uncertainty limiting the ability of a cognitive radio network to reclaim a band for its use. Coordination among nearby cognitive radios is required to control this uncertainty. While this coordination can take a form similar to a traditional MAC protocol for data communication, its role is different in that it aims to reduce the uncertainty about interference rather than just reducing the interference itself. We show how the degree of coordination required can vary based on the coherence times and bandwidths involved, as well as the complexity of the detectors themselves. The simplest sensing strategies end up needing the most coordination, while more complex strategies involving adaptive coherent processing and interference prediction can be individually more robust and thereby reduce the need for coordination across different networks. We also show the existence of a coordination radius wall which limits secondary user densities that can be supported irrespective of coordination involved. Furthermore, local cooperation among cognitive radios for collective decision making can reduce the fading margins we need to budget for. This cooperation benefits from increased secondary user densities and hence induces a minima in the power-coordination tradeoff.

Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs

Sensing is the most important component in any cognitive radio system. The IEEE 802.22 Working Group (WG) is formulating the first worldwide standard for cognitive radios to operate in the television (TV) bands. In order for this standard to succeed, it is imperative that IEEE 802.22 systems employ an effective sensing mechanism to detect the presence of television signals so that it can opportunistically access those bands that are not currently used by TV transmitters. In this paper we highlight the simulation methodology used in the IEEE 802.22 WG and study the performance of the power detector.

Cognitive Radios for Spectrum Sharing [Applications Corner]

The signal processing issues involved in cognitive radios are quite diverse and have led us on a figurative journey from Berkeley, California to Washington D.C. A holistic signal processing perspective shows that while the goal of reducing the regulatory overhead is admirable, everything will have to be put together in a balanced way in order to realize the true potential of this wireless revolution.

Noise Calibration, Delay Coherence and SNR Walls for Signal Detection

In this paper we consider the problem of robustly detecting the presence/absence of signals in uncertain low SNR environments. Our previous results have shown the existence of fundamental SNR thresholds called SNR walls below which robust detection is impossible. For signals with narrowband pilots, we had shown earlier that a simple modification to the matched filter provides significant robustness gains. This technique is called runtime noise calibration. In this paper we show that runtime noise calibration can also be applied to improve the robustness of other feature detectors. We use a 50% duty-cycle pulse-amplitude-modulated signal as an example to illustrate the gains from noise calibration. Robustness results for this example give us important insights which also apply in the general case of cyclostationary signals. Our results suggests that for cyclostationary signals, frequency-selective fading and uncertain noise color are the main reasons for detector non-robustness. Furthermore, we introduce the notion of delay coherence for frequency-selective channels. We also characterize the location of the SNR wall as a function of the delay-coherence time of the channel.

Unified space-time metrics to evaluate spectrum sensing

Frequency-agile radio systems need to decide which frequencies are safe to use. In the context of recycling spectrum that may already be in use by primary users, both the spatial dimension to the spectrum sensing problem and the role of wireless fading are critical. It turns out that the traditional hypothesis testing framework for evaluating sensing misses both of these and thereby gives misleading intuitions. A unified framework is presented here in which the natural ROC curve correctly captures the two features desired from a spectrum sensing system: safety to primary users and performance for the secondary users. It is the trade-off between these two that is fundamental. The spectrum holes being sensed also span both time and space. The single-radio energy detector is used to illustrate the tension between the performance in time and the performance in space for a fixed value of protection to the primary user.

Is interference like noise when you know its codebook

We consider a point to point communication system facing interference from other systems, with a particular focus on the case when this interference is undecodable. It is well known that when the interference is non-interactive, we can certainly treat it as additional noise at the receiver and thereby achieve certain rates. This paper asks whether any higher rates could be achieved by exploiting knowledge of the interferers codebook. The main contribution of this paper is to study the converse: if the interference is undecodable, then we cannot do better than treating it as additional noise. This is proved for almost all interference codebooks when viewed under the random Gaussian codebook measure. When the interference signal is strong enough to be decodable, then codebook knowledge can be exploited at our receiver to allow higher rates to be achieved by appropriately structuring our own codebooks. Finally, we give an example of an interference codebook that cannot be completely decoded, but whose knowledge is still useful. However, this interference codebook is bad from the perspective of the interferers own communication system. This leads us to conjecture that when the interference signal is undecodable, the only interference codebooks worth knowing are those that are not worth using from the interfering systems point of view

Space-Time Metrics for Spectrum Sensing

Cognitive radio systems must robustly sense spectrum holes if they want to use spectrum opportunistically. A traditional sensitivity based time-domain perspective on this sensing problem is relatively straightforward and such sensitivity based sensors are easy to certify. However, this problem is more subtle than it first appears. To really understand the question of {\em what should be the right level of sensitivity for a sensor?}, especially for multi-user sensing algorithms, one is forced to think more deeply about the spatial dimension of sensing and the role of fading. In this paper we propose a framework to model the joint space-time dimension of spectrum sensing. This framework naturally gives us reasonable approximate metrics that capture the two desirable features of a spectrum sensor: safety to primary users and performance for the cognitive radios. It is the tradeoff between these two that is fundamental. This framework helps us to quantify the tradeoff between space and time. By simulating the space-time performance of a single-radio energy detector we see that there is tension between the performance in time and the performance in space for a fixed value of protection to the primary user.

Overcoming SNR walls through macroscale features

We consider the problem of robustly detecting the presence/absence of signals in low signal to noise ratio (SNR) environments. Our previous results have shown the existence of thresholds called SNR walls, below which robust detection becomes impossible due to uncertainties in the environment. These thresholds were shown to exist for many signals used in practice. This paper introduces the idea of macroscale features and shows that they can be used to construct signals that evade SNR walls. In particular, we examine a Gaussian mixture example and a noise-calibration based detection algorithm to show that this signal can be robustly detected in the presence of arbitrarily-varying noise and arbitrarily-varying finite-tap fading processes. Finally, we argue that there is tension between the primary users capacity and the sensing delay experienced by the secondary users. We call this the capacity-delay tradeoff. We derive the capacity-delay tradeoff for the Gaussian example.

Join Minimum Cost Queue For Multiclass Customers: Stability And Performance Bounds

We consider a system of K parallel queues providing different grades of service through each of the queues and serving a multiclass customer population. Service differentiation is achieved by specifying different join prices to the queues. Customers of class j define a cost function pij(ci,xi) for taking service from queue i when the join price for queue i is ci and congestion in queue i is xi and join the queue that minimizes pij(·,·). Such a queuing system will be called the “join minimum cost queue” (JMCQ) and is a generalization of the join shortest queue (JSQ) system. Non-work-conserving (called Paris Metro pricing system) and work-conserving (called the Tirupati system) versions of the JMCQ are analyzed when the cost to an arrival of joining a queue is a convex combination of the join price for that queue and the expected waiting time in that queue at the arrival epoch. Our main results are for a two-queue system.