Swarming Algorithms for Distributed Radio Resource ... - Infocom

[Paolo Di Lorenzo and Sergio Barbarossa]

© istockphoto.com/jamie farrant

Swarming Algorithms for Distributed Radio Resource Allocation

[A further step in the direction of

an ever-deeper synergism between biological mathematical modeling and signal processing]

T

 he major challenge faced by modern communication systems is the striking contrast between the ever-increasing demand for higher data rate links with guaranteed quality of service (QoS), on the one hand, and the scarcity of the radio resources, on the other hand. The solution of this dilemma is the increase of spectral efficiency. Several tools are available for improving spectral efficiency, like adaptive modulation and coding; multiple input, multiple output (MIMO) communications; Digital Object Identifier 10.1109/MSP.2013.2237948 Date of publication: 5 April 2013



cooperative multipoint communications; etc. Among all these tools, it is widely recognized that the approach having the potential for bringing the most significant improvement is spatial reuse of radio resources [1]. This calls for the deployment of base stations covering small cells of radius in the order of a few tens of meters. However, a dense deployment of conventional base stations is impractical because of the high costs of installation and maintenance. The way to overcome this limitation consists of the deployment of heterogeneous networks, which are composed of conventional base stations coexisting with low-cost base stations having very small transmit power and limited complexity. An example is given by

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shows how a simple self-synchronization mechanism can profemtocell networks [2], where femto access points, also called vide the basic tool for implementing distributed maximum home enhanced node B, are installed indoors and cover cells of likelihood estimators in a wireless sensor network. In [8], the radius in the order of tens of meters. They avoid the wall peneauthors develop a decentralized scheduling scheme over a tration losses occurring in outdoor-to-indoor communications network of self-organizing devices that are modeled as pulseand are perfectly compliant with cellular standards. coupled oscillators. Reference [9] describes three applications Consequently, they allow a significant traffic offload from the of bioinspired techniques for resource allocation over wireless to the wired channels, thus freeing important radio cognitive radio networks: detection of spectrum holes; resource resources. This is a win-win strategy for operators and users: allocation in orthogonal frequency-division multiple access The operators get the capability of having more wireless traffic (OFDMA) systems, and distributed resource auctioning. In [10], within a given area, thus increasing revenues; the users have a the authors propose a biologically seamless connectivity to the netinspired consensus-based specwork, with better QoS with nature acts as an open trum sensing technique. A respect to standard Wi-Fi and easbook from which we can nonconvex model for hierarchical ier hand off. However, this solutake inspiration for devising heterogeneous network topology tion does not come without self-organization strategies, control based on flocking algotechnical challenges, the most provided that the natural rithms was recently presented important being interference mechanisms are properly modeled in [13]. management. In fact, while in mathematically and adapted to In this article, we show how conventional networks radio the applications of interest. mathematical models describing resource allocation is typically swarms foraging mechanisms can performed by the base stations, provide an important source of inspiration to devise decentralusing a centralized approach, in the heterogeneous scenario, it ized radio resource allocation mechanisms, which are robust to becomes quite problematic to envisage a centralized approach random disturbances introduced by realistic channels (such as taking into account both conventional and innovative base stafading, quantization, and delay) and intrinsically able to impletions. The major limitation concerning the home base stations ment spatial reuse of frequency slots in a totally decentralized is that they have low complexity and, being owned by the users, fashion. they are not necessarily installed according to a radio optimization criterion and they may be switched on and off at the user’s Dynamic resource allocation will. Given this context, the only viable mechanism is to endow through swarming the network with some sort of self-organization capability, with In our dynamic allocation problem, we distinguish between a particular attention to keep interference under control. Selfmacrousers, i.e., the mobile users served by a macrobase station organization can come in different forms: self-configuration, (MBS) and cognitive users who learn from the environment and self-optimization, self-healing, etc. In particular, since some of adapt their transmission strategy consequently, to make the the nodes have limited complexity and computational capabilibest usage of the available spectral resources, still having a negties, it is fundamental to devise mechanisms such that the ligible impact on macrousers. We assume the presence of M overall network is robust, as a whole, against abrupt and unpredictable changes of network conditions, like interference, cognitive users, embedded in an electromagnetic environment change of connectivity, and so on, even if the individual nodes where there are communications between macrousers and their are not sophisticated enough to handle the problem appropriserving base stations and vice versa. The communications ately. In fact, self-organization features are already introduced among macrousers are perceived as interference from the cogin the current standardization process of the 3rd Generation nitive users. The goal of each cognitive user is to select the Partnership Project long-term evolution leading towards radio resource, e.g., a time/frequency slot that is more approprifourth-generation mobile communication systems [3]. ate, for the given interference environment. Furthermore, the Within this context, nature acts as an open book from cognitive nodes should coordinate with each other to avoid the which we can take inspiration for devising self-organization channels already occupied by macrousers and avoid conflicts strategies, provided that the natural mechanisms are properly with each other. In general, the ideal features we would like to modeled mathematically and adapted to the applications of enforce on our cognitive network are the following: interest. Not surprisingly, in fact, recent years have witnessed 1) Decentralized radio resource allocation mechanism: This a series of works where several network design strategies took feature is fundamental to avoid the need for a centralized inspiration from biological system models [4]–[15]. Two intercontrol, which would be very difficult to enforce in an hetesting special issues on bioinspired algorithms for networking erogeneous network, and to increase network robustness applications are [4] and [5], covering a wide range of potential against individual node failures. applications, from routing, scheduling, cognitive radios, and 2) Cooperative sensing: Sensing is the first prerequisite of wireless sensor networks, just to name a few. A useful survey any system capable of adaptation and learning from the article is [6], encompassing several aspects. Reference [7] environment; in the particular application of interest in



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this work, individual sensing is prone to local shadowing effects that could mask the presence of interference. This is why it is useful to incorporate cooperation among the sensing nodes. 3) Local coordination: In recent years, a decentralized system has often been thought as a system of competitive agents fighting over the same pool of resources. In fact, game theory has played a key role in devising decentralized (competitive) mechanisms for resource allocation [17]. However, pure competition can lead to inefficient Nash equilibria. To overcome this drawback, several forms of coordination have been proposed, based on the local interaction among nearby nodes who exchange some control parameters (so-called prices) in the sake of improving performance with respect to purely competitive games. 4) Spatial reuse of time/frequency slots: Among many alternatives, like adaptive modulation/coding, cooperative communications, etc., it is widely acknowledged that one of the factors that induce the most significant increase of spectral efficiency is spatial reuse of radio resources [1]. 5) Parsimonious usage of the radio resources, commensurate to the required QoS: While devising a decentralized radio resource allocation strategy, it is important to avoid an excessive spread over both time and frequency axes. 6) Resilience to unpredictable channel failure: The interaction among the cognitive users occur over realistic channels, which are subject to unpredictable failures due to fading, packet drop, quantization, etc. Hence, any mechanism able to limit the effects of these impairments over the radio resource allocation strategy is welcome. Clearly, it is not easy to satisfy all these requirements with a decentralized system. Nevertheless, we show next that a swarming algorithm borrowed from biological models can help us envisage a mechanism that, although in a simple form, encompasses most of the items listed above. Of course, the proposed approach is not “the solution” to all the issues listed above. Nevertheless, it is a first step towards the formulation of the problems raised above within a common framework that draws inspiration from biological systems and, as such, it naturally brings a self-organization principle into the solution of the problem. Our approach builds upon the formulation of the overall problem as the minimization of a network utility function. This approach requires an inevitable simplification of the problem at hand, but it offers the possibility for a rigorous understanding of the problem and it helps to suggest further refinements and future developments. Given M cognitive nodes, each node is identified by two vectors, ri and x i, indicating, respectively, the spatial position of node i and the location of the radio resource assigned to node i. The vector ri is then a three-dimensional vector, defined in the space coordinates, while the vector x i has dimensions dictated by the resource domain over which the resources are to be allocated. For instance, if the node allocates power over a time-frequency grid, the vector x i is a two-dimensional (2-D) vector whose



entries are the positions of the time slot and the frequency channel allocated to node i. We indicate by I i (x i) the interference sensed by node i over the resource block associated to vector x i . We formulate the overall resource allocation problem as the distributed minimization of a global function, which we will denote as the potential function, defined as follows: M

M

  J (x) = / I i (x i) + 1 / 2 i =1 i =1

M

/ a ij [J a (< x j - x i