Signal reuse for multiple purposes is a way to increase spectrum utilization. In this paper, we leverage the WiFi signals of opportunity for the sensing purpose. The spectrograms derived from WiFi downlink (DL) signals reflected from cars are used as fingerprints to efficiently infer car density in parking lots. To achieve this, experimental measurements were conducted in a real outdoor environment to probe the reflected WiFi signals from targets (cars), and the collected datasets are employed for density estimation. The estimator combines hybrid convolutional neural network (CNN) and support vector machine (SVM) for classification, along with least-square estimate (LSE) for interpolation. The probed signals are influenced by many factors, such as the number of WiFi users and data traffic, thereby degrading the estimation accuracy. To address these challenges, we propose an uplink-downlink (UL-DL) WiFi identification and separation technique using the least absolute shrinkage and selection operator (LASSO) technique, without requiring coordination with WiFi access points. Compared to the estimation using a mixture of UL-DL WiFi signals, the simulation results demonstrate that the proposed method achieves significant improvement in estimation accuracy.

Passive sensing leverages existing signals from illuminators of opportunity to perform target localization, detection, and tracking without the need for additional infrastructure. In this paper, we explore the use of WiFi signals of opportunity for estimating car density in parking lots. The aim is to develop an efficient and cost-effective parking occupancy estimation system to alleviate traffic congestion caused by drivers searching for parking spaces. To achieve this, experimental measurements were conducted in a real outdoor environment to collect reflected WiFi signals from targets (cars). These collected signals were then used for car density estimation using a combination of semi-supervised learning convolutional neural network (CNN) and weighted-centroid interpolation techniques, only requiring small size datasets and limited measurements. The proposed method overcomes the limitations of existing data-driven estimators by reducing the reliance on large labeled datasets and computational complexity associated with traditional supervised learning methods. In addition, it provides a cost-effective alternative to the traditional systems that rely on a large number of sensors. Simulations are performed to evaluate the performance of the estimation, and the results demonstrate that our scheme can effectively estimate car densities in the parking lot with reasonable estimation errors.

In this paper, we propose and study a particular use case capable of performing radio-based traffic density estimation for adaptive beam allocation. The proposed scheme explores the synergy between communication and sensing from an Integrated sensing and communication (ISAC) perspective. The traffic den-sity estimation is aided by communication functionality, which involves reusing communication waveforms and utilizing multi-beam forming and sweeping techniques. Meanwhile, the sensing outcomes assist in proactively allocating radio beams. There have been accurate traffic monitoring methods relying on a large number of detectors. However, these traditional techniques have some shortcomings, and it is necessary to explore alternative traffic density estimation approaches. In this regard, we exploit orthogonal frequency division multiplexing (OFDM) communication signals of opportunity reflected from targets (vehicles) to estimate the traffic density of a road section by using Jensen-Shannon (JS) divergence and weighted-centroid interpolation based on a few samples of density scenarios. Then, we present a millimeter-wave (mmWave) adaptive beam allocation protocol based on the traffic density estimation to enhance communication coverage for the vehicular users in the area of interest. The simulation results demonstrate that our traffic density estimation can handle a wide range of targets with a relatively low estimation error. In addition, the analysis of the adaptive beam allocation shows that it effectively improves the quality of service (QoS, in terms of outage probability) of the communication system.

Wireless network virtualization is emerging as a promising technology for cellular networks. A key advantage of introducing virtualization in cellular networks is that wireless services can be decoupled from network resources (e.g., infrastructure and spectrum) so that multiple virtual networks can be built using a shared pool of network resources. This paper develops an optimization framework for orchestrating virtualized cellular networks while enabling and exploiting statistical multiplexing. Our proposed framework has two phases: virtual network deployment (static) and statistical multiplexing (adaptive). In the virtual network deployment phase, network resources are aggregated, sliced, and allocated to the virtual networks considering the presence of uncertainty in user equipment (UE) locations and channel conditions, without knowing which realization of UE locations and channel conditions will occur. Once the virtual networks are deployed, each of the aggregated base stations (BSs) performs statistical multiplexing, i.e., allocates excess resources from the over-satisfied slices to the under-satisfied slices, according to the realized channel conditions of associated UEs. Our numerical results demonstrate that the proposed framework outperforms existing virtualization frameworks in terms of probabilistically satisfying virtual networks’ rate and coverage demands while minimizing resource over-provisioning, in the presence of uncertainty in UE locations and channel conditions.

Interferometry is a powerful tool for estimating the incident angle of electromagnetic (EM) waves by calculating the correlation of received signals at different antennas. Motivated by very-long-baseline interfereometry (VLBI) in radio astronomy, an interferometry based sensing scheme is proposed as integrated sensing and communications (ISAC). It reuses the communication signal from base stations (BSs), similarly to passive radars, which improves the sensing precision and spectrum efficiency. Different from the almost-perfect synchronization in VLBI realized by atomic clocks, the synchronization in BSs of cellular commu-nication networks (usually based on GPS signals) could have significant errors. Therefore, algorithms for compensating for synchronization errors in both time and frequency are proposed. Numerical simulations demonstrate that the proposed algorithms can substantially alleviate the synchronization errors.

Millimeter wave (mmW) systems typically use beamforming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, we propose a joint optimization framework to determine the minimum number of required access points (APs), their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to maintain a network-wide signal-to-noise ratio (SNR) coverage and stable connections. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. We develop our joint optimization framework following a two-stage chance-constrained stochastic optimization model. Our numerical results demonstrate the gains brought by our proposed framework in terms of reducing cost of network deployment while ensuring a network-wide SNR coverage and stable connections under various UE distributions and system parameters.

Millimeter wave (mmW) systems typically use beamforming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, assuming that mmW access points (APs) use low complexity fixed directional antennas, we propose a joint optimization framework for AP deployment and UE assignment in indoor mmW networks. The goal of our optimization framework is to determine the minimum number of required APs, their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to ensure network-wide coverage and maximize the stability of mmW beams assigned to individual UEs. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. Our numerical results demonstrate the behavior of the proposed framework under various UE distributions and system parameters.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Wireless network virtualization is emerging as a promising technology for next-generation (5G) cellular networks. A key advantage of introducing virtualization in cellular networks is that wireless services can be decoupled from network resources (e.g., infrastructure and spectrum) so that multiple virtual networks can be built using a shared pool of network resources. This paper develops a theoretical framework for optimizing the resource allocation in virtualized cellular networks with heterogeneous coverage requirements. Specifically, we first formulate a chance-constrained virtual resource allocation problem that aims at probabilistically guaranteeing virtual networks' downlink coverage and rate demand satisfaction while minimizing resource over-provisioning in the presence of uncertainty in user equipment (UE) locations and channel conditions. Thereafter, we derive a closed-form expression for the downlink rate coverage probability of a typical virtual network. With the closed-form expression, we design an efficient algorithm to solve the chance-constrained problem with affordable computation complexity. Furthermore, considering the possibility of lack of sufficient network resources to satisfy all virtual networks' demands, we design a prioritized virtual resource allocation scheme where virtual networks are built sequentially based on their given priorities. Our results demonstrate that the proposed stochastic virtualization framework outperforms existing deterministic virtualization frameworks in terms of probabilistically guaranteeing virtual networks' coverage and rate demand satisfaction.

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demands using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least β, and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The proposed framework exploits the special structure of the problem through decomposition into two subproblems. A particle swarm optimization (PSO)-based algorithm is tailored to the first-stage problem in order to obtain good solutions. The second-stage problem is further decomposed into several subproblems that can be solved independently in parallel. Our numerical results (i) demonstrate the advantages of stochastic compared to deterministic allocation, (ii) illustrate that the proposed framework reaches the optimal solution for the two-stage problem in few iterations, and (iii) explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.

Wireless network virtualization is emerging as an important technology for next-generation (5G) wireless networks. A key advantage of introducing virtualization in cellular networks is that service providers can robustly share virtualized network resources (e.g., infrastructure and spectrum) to extend coverage, increase capacity, and reduce costs. {However, the inherent features of wireless networks, i.e., the uncertainty in user equipment (UE) locations and channel conditions impose significant challenges on virtualization and sharing of the network resources.} In this context, we propose a stochastic optimization-based virtualization framework that enables robust sharing of network resources. Our proposed scheme aims at probabilistically guaranteeing UEs' Quality of Service (QoS) demand satisfaction, while minimizing the cost for service providers, with reasonable computational complexity and affordable network overhead.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD). Although operating in FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the coexistence problem of concurrent transmissions between multiple FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex mode. First, we consider two links and formulate the interactions between them as a Bayesian game. In this game, each link tries to maximize its throughput while minimizing the transmission power cost. We derive a closed-form expression for the Bayesian Nash equilibrium and determine the conditions under which no outage occurs at either link. Then, we study the coexistence problem between more than two links, assuming that each link is only affected by its dominant interfering link. We show that under this assumption, no more than two links will be involved in a single game. Finally, we corroborate our analytical findings via extensive simulations and numerical results.

There are different forms of uncertainty in satellite communications, including cognitive interferers, channel conditions, packet traffic, and spectrum occupancy of users across channels. In addition, delay (such as propagation delay observed over satellite links) increases spectrum uncertainty and makes spectrum sensing and spectrum access two challenging tasks. To address such challenges, this paper presents a regret minimization solution for primary user (PU) and secondary user (SU) spectrum access to satellite resources in the presence of cognitive interferers. This robust game theoretic solution supports hierarchical spectrum sharing and dynamic spectrum access over multiple channels. Users select channels for data transmission and perform power control to optimize individual utility functions that are random due to different forms of uncertainty. The proposed game engine based on regret minimization framework provides a low-complexity and fast solution compared with traditional game solutions based on expected utility maximization. Detailed numerical results evaluate throughput and delay of PUs and SUs in the presence of cognitive interferers and compare the robust game theory-enabled approach with two benchmark schemes (with and without knowledge on channel availability). To support controllable and repeatable test and evaluation with real radios, an emulation testbed is built with software-defined radios connected with a network channel emulator that generates channel, mobility, and interference effects for satellite communications. GNU Radio modules are developed for cognitive network functionalities and run on USRP N210 radios that represent SU, PU, interferer, and satellite nodes. Emulation tests validate the effectiveness of the proposed solution under real radio effects.

Software-defined cellular networks (SDCN) have been recently introduced to enable flexible cellular network design that facilitates fulfilling 5G design requirements. Placement of controllers within the SDCN plays a crucial role in optimizing its performance. In this paper, we study the controller placement problem in SDCN, considering the uncertainty in cellular user locations. Specifically, our contributions are as follows. First, we develop C3P2, a static joint stochastic controller placement and evolved node B (eNB)-controller assignment problem. The objective of C3P2is to minimize the number of controllers needed to control all eNBs, while ensuring that the response time to each eNB will exceed seconds with probability less than 1-β. Second, we develop CPPA, a joint stochastic controller placement and adaptive eNB-controller assignment problem. In contrast to C3P2, in CPPA the eNB-controller assignment adapts to variations in the eNB request rates, resulting from the variations in the cellular user locations. Finally, we use sample average approximation combined with various linearization techniques to solve and evaluate C3P2and CPPA under various system parameters. Our results demonstrate the advantages of (i) joint compared to sequential optimization, (ii) stochastic compared to deterministic optimization, and (iii) adaptive compared to static optimization.

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. Optimizing the network performance under deterministic demands has been well-studied. It is still an open question of how to optimally utilize available frequency bands under uncertainty in AP demands. We propose two approaches to tackle this problem using a stochastic optimization framework. First, we develop a static joint stochastic center frequency and bandwidth allocation scheme. The goal of this scheme is to minimize the total occupied bandwidth while satisfying the demand of each AP with probability at least ß. Second, we develop a two-stage joint stochastic center frequency and adaptive bandwidth allocation scheme. In contrast to the static scheme, the two-stage allocation scheme allows adaptability of the bandwidth allocated to each AP in response to its demand variation, while keeping the total occupied bandwidth fixed. Our numerical results demonstrate the advantages of (i) stochastic compared to naive allocation and (ii) adaptive compared to static allocation. They also explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.

To cope with increasing demand on wireless services, next-generation wireless systems are expected to use multiple radio access technologies, with different receive and transmit characteristics, operating over the same band of spectrum in a spatial-temporal neighborhood. This will make the RF front-ends susceptible to unprecedented adjacent-channel interference (Ad), which can jeopardize communication performance. In this paper, we propose a novel ACI-aware joint channel and power allocation framework that takes into account the receiver imperfections arising due to (i) imperfect image frequency rejection, and (ii) analog-to-digital converter aliasing. The proposed resource allocation framework aims at minimizing the number of allocated channels and the aggregate power transmitted while satisfying the rate demands of different links in a multi-RAT environment. The results demonstrate the criticality of receiver-characteristic awareness when designing resource allocation schemes for different types of networks. Also, the trade-off between channel allocation and power assignment is explained.

Millimeter wave (mmW) communication is a promising solution for providing high-capacity wireless network access. However, the benefits of mmW are limited by the fact that the channel between a mmW access point and the user equipment can stochastically change due to severe blockage of mmW links by obstacles such as the human body. Thus, one main challenge of mmW network coverage is to enable directional line-of-sight links between access points and mobile devices. In this paper, a novel framework is proposed for optimizing mmW network coverage within hotspots and in-venue regions, while being cognizant of the users' orientation. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is assumed to be stochastic. Hence, a joint stochastic access point placement and beam steering problem is formulated, under desired network coverage constraints. Then, a greedy algorithm is introduced to solve the joint deployment and assignment problem using a “size constrained weighted set cover” approach. A closed-form approximation ratio between the optimal and approximate solutions is analytically derived. Simulation results show that, in order to guarantee coverage constraint for the Alumni Assembly Hall of Virginia Tech, the greedy algorithm uses at most one more AP compared to the optimal solution. The results also show that, due to the use of an additional AP, the greedy algorithm will yield a network coverage that is about 8% better than the optimal, AP-minimizing solution.

Software-defined networking (SDN) abstracts and centralizes the network control functions in a software entity that runs on a server, known as SDN controller. The controller needs to respond to its controlled elements in a strictly timely manner, and the controller placement has a prominent effect on its response time. Originally, all SDN architectures assumed a physical wired connection between the SDN controller and its controlled elements, and the controller placement problem (CPP) has been only studied under such wired settings. Recently, novel SDN architectures have been proposed in which a direct wireless connection is assumed between the controller and its controlled elements. In this paper, we consider the 'wireless CPP,' when the link between the controller and the controlled element is wireless. Specifically, our contributions are as follows. First, we propose two joint controller placement and assignment formulations, assuming wired links between the controllers and their controlled elements; the first formulation considers an average response time constraint, whereas the second one considers a per-link response time constraint. Then, using chance- constrained stochastic programming (CCSP), we extend our formulation to the case when the links between the controllers and their controlled elements are wireless. Finally, we evaluate our joint placement and assignment schemes under various system parameters. Our results demonstrate the advantage of our joint scheme, in terms of reducing the required number of controllers, compared to a recent sequential assignment and placement scheme in the literature. They also show the ability of our CCSP-based scheme in probabilistically satisfying the controllers response time constraints.

Since LTE in unlicensed spectrum (LTE-U) was proposed by Qualcomm, it has drawn considerable interest because of its potential to increase the capacity of existing LTE networks by utilizing existing infrastructure in the unlicensed band. But, Wi-Fi technology, already operating in the unlicensed 5 GHz band, creates several potential challenges for managing the activities of these two different technologies in the same band. In this context, we propose an adaptive coexistence scheme between LTE and WiFi by utilizing almost blank subframes (ABS). An ABS is an LTE subframe of duration 1 ms (containing two time slots of 0.5 ms duration) with reduced downlink activity. LTE allocates ABSs over 20 MHz channels in 5 GHz band to allow WiFi to access the spectrum. In the proposed coexistence scheme, each LTE cell optimally distributes ABSs over the frame to provide certain quality of service (QoS) guarantees for WiFi traffic while ensuring the performance of its own users.

Millimeter wave (mmWave) communication is a promising solution for providing high capacity wireless access to regions with high traffic demands. The main challenge of mmWave communications is the availability of directional line of sight links between access points and mobile devices which stochastically change due to high attenuation in mmWave propagation and severe blockage of mmWave links with obstacles such as human bodies. In this paper, a novel framework for optimizing the deployment of mmW access points, while being cognizant of the mobile devices orientation, is proposed. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is changing stochastically. To minimize the number of access points while satisfying the line of sight coverage of mobile devices, first, a joint access point placement and mobile device assignment problem is proposed, assuming that the orientation of each user is deterministically known. This formulation is then extended to the case in which the orientation of the user is stochastic. Finally, the proposed deterministic and stochastic joint access point placement and mobile device assignment schemes are evaluated under various system parameters. Simulation results demonstrate the advantage of the proposed stochastic scheme to the deterministic scheme, in terms of reducing the load on access points. Moreover, on average, the proposed stochastic scheme can increase the probability of user satisfaction up to 24% for 0.95 requested coverage probability compared to the deterministic case.

Dynamic spectrum access (DSA) has emerged as an enabling technology to allow more intensive sharing of the radio spectrum. A requirement for most proposed DSA techniques is prior knowledge of the primary user's access pattern or the ability to predict primary user activities. Therefore, spectrum surveys are taking place on an even wider scale to provide data on spectrum usage and occupancy for developing new prediction models and for spectrum planning by regulators. This paper investigates the potential of mining spectrum data for correlation between human activities in a neighborhood and the resulting spectrum occupancy across different bands. We propose a systematic approach based on two clustering techniques: Gaussian mixture models (GMMs) and self-organizing map neural networks (SOMNNs). We mine spectrum measurements gathered by our network of spectrum observatories in Virginia and Illinois. The results confirm the existence of observable correlation and show that our proposed techniques detect correlation across various land mobile radio (LMR) and cellular bands under a wide range of scenarios with a high detection ratio. These results inspire us to develop more efficient prediction models for applications in opportunistic spectrum access (OSA) or self-organized networks.

Resource sharing in mobile wireless networks has been employed to reduce costs, extend coverage, and ease the entry of new players in the market. The introduction of programmability and virtualization is expected to amplify these benefits of resource sharing. In this paper, we study a new virtualization-based paradigm for resource sharing in mobile wireless networks. Specifically, we consider the problem of resource allocation, particularly when user demands are uncertain. We formulate several two-stage sequential stochastic allocation schemes that provide tradeoffs between cost and user satisfaction. These allocation schemes are studied under different resource provider pricing models. Our simulations demonstrate that: First, while reducing cost significantly, virtualization considerably improves user satisfaction, and virtualization gains increase with the number of operators that share resources. Second, the improvements in cost, user satisfaction, and resource usage increase substantially with the level of user clustering.

This paper presents a game theoretic solution for hierarchical spectrum sharing between primary users (PUs) and secondary users (SUs) in the presence of cognitive interferers. There exist several forms of uncertainty, including channel conditions, packet traffic, and spectrum occupancy of users across channels. This uncertainty is further aggravated by delays (such as propagation delays observed over satellite links) that make spectrum-efficient communication a challenging task. A robust game theoretic framework is developed for dynamic spectrum access (DSA) management over multiple channels. Cognitive functionalities employed in the game solution include selecting channels for data transmission and performing power control at each user to sustain target rates. By considering random utility functions, the game engine based on regret minimization provides low complexity and fast solutions compared to traditional game solutions based on expected utility maximization. Detailed numerical results with comparison to benchmark schemes (the ideal case and the random case where users have perfect or no knowledge on channel availability, respectively) are provided to show the effectiveness of robust game theory-enabled approach.

Two promising solutions have been recently proposed to address the massive growth in mobile traffic and wireless devices: LTE-U and in-band full-duplex (FD) wireless. LTE-U extends the benefits of LTE-A to the unlicensed 5 GHz band, used mainly by Wi-Fi users. However, the uncertainty in Wi-Fi user activities makes provisioning QoS guarantees to LTE-U users challenging. On the other hand, FD wireless can double spectrum efficiency by enabling simultaneous transmission and reception over the same frequency band. Our objective in this paper is to exploit excess capacity of deployed Wi-Fi networks (operating in the 5 GHz band) to orchestrate a ‘robust’ virtual LTE-U network from a hybrid set of half-duplex (HD) and FD Wi-Fi access points (APs). Although the orchestrated LTE-U network does not support deterministic QoS guarantees, it is designed to provide prespecified probabilistic QoS guarantees (hence, it is robust). Towards achieving our goal, we develop novel stochastic resource allocation formulations that optimally orchestrate a virtual LTE-U network from a hybrid set of HD/FD APs with the minimum cost. We first consider the single small-cell problem and propose a stochastic formulation, which we refer to as CCLTEUsingle. Then, we study the multi-cell stochastic allocation problem and develop another formulation, which we refer to as CCLTEUmulti. Our formulations adopt a ‘chance-constrained stochastic programming’ approach. We derive the deterministic equivalent programs of CCLTEUsingleand CCLTEUmultiand evaluate them numerically under various system parameters.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD) communications. Although operating in a FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the problem of concurrent transmissions between two FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex (HD) mode. Following a game-theoretic framework, we aim to determine the stable behavior (FD or HD) for the two coexisting links. To achieve this objective, we first analyze a simple normal form game between the two links, which provides some insight into the coexistence problem. It turns out that the outcome of this game depends on two factors: The amount of residual self-interference (due to imperfect SIC) and the external interference from one link on the other. To capture the impact of residual self-interference, we formulate a Bayesian game between two links with heterogeneous SIC capabilities. In this game, each link (player) tries to maximize its throughput while minimizing the transmission power cost. We derive the Bayesian Nash equilibrium for this game. Furthermore, we determine the conditions on the external interference under which no outage occurs at both links. Finally, we conduct simulations and USRP hardware experiments to corroborate our analytical findings.

This paper describes the Flexible Internetwork Stack (FINS) Framework, an open-source tool to enable implementation-based experimental research in computer networking. The FINS Framework uses a module-based architecture that allows cross-layer behavior and runtime reconfiguration of the protocol stack. The FINS Framework is general enough to enable experimental setups under various network architectures (e.g., MANET, infrastructure, mesh) and to accelerate prototyping solutions for evolving areas (e.g., cognitive networks, cross-layer design, context-aware applications). Version 1.0 of the framework makes use of existing physical and data link layer functionality, while enabling modifications to the stack at the network layer and above, or even the implementation of a clean-slate, non-layered protocol architecture. Protocols, stubs for communicating with intact layers, and management and supervisory functions are implemented as FINS Framework modules, interconnected by a central switch. This paper describes the FINS Framework architecture, presents an initial assessment along with experiments enabled by the tool, and documents an intuitive mechanism for transparently intercepting socket calls that maintains efficiency and flexibility. Performance testing shows that the FINS Framework is capable of supporting experiments requiring IEEE 802.11g hardware speeds and operating in varying networking architecture and experimental scenarios on both Ubuntu laptops and Android devices.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

The volume of video streaming traffic over the Internet has increased dramatically over the last 10 years. By the end of 2014, it is expected that 77% of the US internet traffic will be video streaming [1]. In parallel, the last ten years have seen an incredible deployment of IEEE 802.11 technologies, also known as WiFi, at homes and public places. Although, the industry and research communities have given a lot attention to address the challenges of video streaming over wireless access networks (e.g., Cellular, WiMAX, WiFi), there are still interesting problems to address to enhance the user quality of experience (QoE). In this paper, we highlight the challenges that face adaptive video streaming over wireless access networks. Then, we propose a a heuristic cross-layer adaptation algorithm named E2E-MAC to improve the video client application response to changing conditions in WiFi environments. The algorithm, which runs on the client side, combines end to end (E2E) throughput measurements with MAC-based measurements from the WiFi link to choose the appropriate streaming quality-to request from the server. Simulation results have shown that our algorithm reduces the occurrence of buffer under-run, increases the average achieved streaming quality, and lowers the rate of lost video frames. This is achieved with a minor overhead on the client application's side.

Implementation of carrier sensing-based medium access control (MAC) protocols on inexpensive reconfigurable radio platforms has proven challenging due to long and unpredictable delays associated with both signal processing on a general purpose processor (GPP) and the interface between the radio frequency (RF) front end and the GPP. This paper describes the development and implementation of a split-functionality architecture for a contention-based carrier sensing MAC, in which some of the functions reside on an field-programmable gate array (FPGA) and others reside in the GPP. We provide an FPGA-based implementation of a carrier sensing block and develop two versions of a carrier sense multiple access (CSMA) MAC protocol based upon this block. We experimentally test the performance of the resulting protocols in a multihop environment in terms of end-to-end throughput and required frame retransmissions. We cross-validate these results with a network simulator with modules modified to reflect the mean and variance of delays measured in components of the real software-defined radio system.

In this paper, we present an economic model for offloading data from subscribers of a large scale cellular operator to a small scale WLAN in a multi-hop cellular environment. We make use of a hexagonal tessellation deployed with relay elements to model the multi-hop capability. An incentive-based model helps to determine the behavior of the cellular and WLAN operator, as the cellular operator decides to offload its users depending upon the price charged by the WLAN operator for each offloaded user. The simulated results quantify the benefits of collaboration between the operators in terms of the offload ratio, network efficiency, and revenue gains.

With increasing mobile data demand there is a push towards heterogeneous networks. Small-scale operators (SSOs) of WLANs are becoming more prevalent, while Mobile Network Operators (MNOs) seek an outlet for their customers' data usage. These conditions prompt the need for an effective relationship between the two parties for the purpose of offloading cellular data traffic to WLANs in a way that is economically beneficial to all involved. This paper presents a model of such a relationship, in which the SSO sets a strategic offloading price per subscriber and the MNO chooses how many subscribers it wants to offload in order to minimize its costs. The application of this model is simulated in a real-world WLAN deployment in Oulu, Finland. Our findings can be used by both MNOs and SSOs to make informed network deployment decisions, even before engaging in an offloading relationship.

The advancement of cognitive radio (CR) has uncovered new dynamics in multi-hop, wireless networking. Given the increased agility of a transceiver's frequency assignment, the network topology can be optimized to address end-to-end networking goals. We propose a channel assignment scheme for cognitive radio networks (CRNs) that balances the need for topology adaptation focusing on flow rate maximization and the need for a stable baseline topology that supports network connectivity. We focus on CRNs in which nodes are equipped with multiple radios or transceivers, each of which can be assigned to a channel. First, our approach assigns channels independently of traffic, to achieve basic network connectivity and support light loads such as control traffic, and second, it dynamically assigns channels to the remaining transceivers in response to traffic demand. In this paper, we focus on the traffic-independent (TI) channel assignment with the goal of dedicating as few transceivers as possible to achieving baseline connectivity. By conserving transceivers in the TI assignment, the network is more able to adapt to any traffic demands in a subsequent traffic-driven (TD) assignment. We formulate the problem as a two-stage mixed integer linear program (MILP), with a TI stage and a TD stage. We propose a centralized greedy approach to TI assignment which performs nearly identically to the optimum obtained from the two-stage MILP in terms of the number of transceivers assigned and flow rate in the evaluated scenarios. Subsequently, we propose a distributed greedy TI approach that performs within 9% of the optimum in terms of the number of transceivers assigned and within 1.5% of the optimum in terms of flow rate.

In this paper, altruistic coalition formation in cooperative relay networks is studied. The communication is performed over two phases, the broadcasting phase and the cooperation phase. In the broadcasting phase, each node broadcasts its signal in its time-slot, while in the cooperation phase, all the nodes within their coalitions simultaneously relay each others' signals. A distributed merge-and-split algorithm is proposed to allow nodes to form coalitions and improve their total achievable rate. Moreover, the impact of different power allocation criteria is studied, where the sum-of-rates maximizing power allocation is shown to promote altruistic coalition formation and results in the largest coalitions among the different power allocation criteria. Finally, the proposed algorithm is compared with centralized power allocation and coalition formation, and shown to yield a good tradeoff between network sum-rate and computational complexity.

In this paper, a comparative study of network-coded bi-directional amplify-and-forward (BD-AF) relaying is presented. In bi-directional relay networks, communication is performed over two phases: the broadcasting phase, and the cooperation phase. In the broadcasting phase, both source nodes broadcast their signals simultaneously to the N relay nodes, while in the cooperation phase, transmission is based on one of two modes: (1) time-division (TD), or (2) multiple-access (MA). In the TD-BD-AF scheme, each relay node is allocated a time-slot to transmit its processed signal, while in the MA-BD-AF scheme, all the N relay nodes simultaneously transmit network-coded signals to both source nodes, in a single time-slot. Moreover, a suboptimal relay selection (i.e. SRS-BD-AF) that approximately maximizes the sum-of-rates is proposed. Optimal and suboptimal sum-of-rates maximizing power allocations are studied under the TD-BD-AF and MA-BD-AF schemes, respectively, where it is shown that the MA-BD-AF scheme reduces to the SRS-BD-AF scheme. Symbol error rate performance analysis is provided, where it is shown that both the TD-BD-AF and SRS-BD-AF schemes achieve full diversity. Imperfect timing synchronization is analyzed and it is demonstrated that the SRS-BD-AF outperforms the other schemes in terms of the achievable rate. Simulation results are provided to complement the theoretical analysis.

In this paper, a performance analysis of network-coded bidirectional amplify-and-forward (BD-AF) multi-relay networks is presented. In such networks, communication is performed over two phases: the broadcasting phase, and the cooperation phase. In the broadcasting phase, both source nodes broadcast their signals simultaneously to the intermediate JV relay nodes. The cooperation phase is based on one of two schemes: (1) multiple-access (MA) and (2) single relay selection (RS) transmission. In the MA-BD-AF scheme, all N relay nodes simultaneously transmit linearly coded signals to both source nodes. A simple suboptimal relay selection scheme (i.e. SRS-BD-AF) is proposed such that the relay that maximizes the sum-of-rates is selected to transmit its network coded signal to both source nodes. Finally, a suboptimal sum-of-rates maximizing relay power allocation under the MA-BD-AF scheme is formulated and shown to reduce to the SRS-BD-AF scheme. Also, a symbol error rate performance analysis is provided, where it is shown that the SRS-BD-AF scheme achieves full diversity. Simulation results are provided to complement the theoretical analysis.

In this paper, the impact of cooperative power allocation on distributed altruistic coalition formation in cooperative relay networks is studied. Particularly, equal power allocation (EPA), maxmin rate (MMR) and sum-of-rates maximizing (SRM) power allocation criteria are considered. A distributed merge-and-split algorithm is proposed to allow network nodes to form coalitions and improve their total achievable rate. The proposed algorithm is compared with that of centralized power control and coalition formation, and is shown to yield a good tradeoff between network sum-rate and computational complexity. Finally, numerical results illustrate that the SRM power allocation criterion promotes altruistic coalition formation and results in the largest coalitions among the different power allocation criteria.

In wireless ad hoc networks, nodes may act selfishly to preserve their limited energy resources. This behavior can cause the network performance to drop. Therefore, cooperation between peers is necessary to keep ad hoc networks operational. Beside the need to actively encourage cooperation, passive encouragement is also needed to overcome the effect of factors that may limit cooperation. These factors include malicious behavior, environmental obstruction that may cause communication distortion, and mobility. In particular, recent studies on real networks have shown a detrimental effect of mobility on network topology that may hinder cooperation in ad hoc networks. Coalitional game theory has been used in the literature to model cooperation in ad hoc networks, yet the effect of mobility has not been studied thoroughly. In this paper, we propose a coalition game model for cooperation in mobile ad hoc networks (MANETs) that shows that the effect of topology changes caused by mobility on the coalitional structure can be mitigated while maintaining coalitional stability. We use the notion of reachability to evaluate the proposed model. We simulate the model under different speeds and node densities. Our simulations show that reachability can be sustained at stable levels despite the deterioration caused by mobility.

In this paper, power allocation for multi-source multi-relay cooperative wireless networks is considered. An ascending-clock auction algorithm is proposed to efficiently allocate cooperative relay power among multiple source nodes in a distributed fashion. In particular, each source node reports its optimal power demand to each relay node based on the relays' announced prices. It is proven that the proposed auction algorithm enforces truthful power demands and converges in a finite number of time-steps to the unique Walrasian Equilibrium allocation that maximizes the sum of utilities. Numerical results are presented to supplement the theoretical analysis and demonstrate the efficiency of the proposed distributed relay power allocation algorithm.

In a wireless network with multiple amplify-and-forward (AF) nodes, the many-to-many cooperative communication is achieved through the novel concept of space-time network coding with optimal node selection (STNC-ONS). The communication under the STNC-ONS scheme is split into two phases: 1) the broadcasting phase (BP) in which each node in its allocated time-slot broadcasts its data symbol to all the other nodes in the network, and 2) the cooperation phase (CP) in which the optimal node corresponding to each desired data symbol is selected for relaying it to the destination node in its own time-slot. The optimal node selection is based on the maximum harmonic mean value of the source, intermediate, and destination nodes' scaled instantaneous channel gains. An approximate symbol-error-rate (SER) expression for M-ary phase shift keying (M-PSK) modulation is derived along with an upper bound SER approximation which is shown to be asymptotic at high signal-to-noise ratio. The derived analytical expressions verify that for a network of N nodes, a full diversity order of (N - 1) per node is achieved by the STNC-ONS scheme. It is concluded that the STNC-ONC scheme is a potential many-to-many cooperative communication scheme with applications spanning sensor and mobile wireless networks.

This mini workshop is organized to provide an interactive forum for the introduction of a set of five new curriculum modules developed under IEEE's Real World Engineering Projects (RWEP) program. The modules, which are representative of a larger collection of curriculum modules available to the public via an open-access RWEP web portal, are designed for use in the first-year engineering and computer science classroom, and are hands-on, team-based projects that emphasize the societal impact of the work that engineers do. After a brief introduction to the RWEP program and the five showcased curriculum modules, the authors of the modules will work one-on-one with the audience providing tutorials on the laboratory activities associated with their modules in a highly interactive, simultaneous mode.

In this paper, multi-source multi-relay power allocation in cooperative wireless networks is considered. An ascending-clock auction is proposed to efficiently allocate cooperative relay power among multiple source nodes in a distributed fashion. In particular, each source node reports its optimal power demand to each relay node based on the relays' announced prices. It is proven that the proposed auction algorithm enforces truthful power demands and converges in a finite number of time- steps to the unique Walrasian Equilibrium allocation that maximizes the social welfare. Numerical results are presented to supplement the theoretical analysis and demonstrate the efficiency of the proposed distributed relay power allocation algorithm.

In this paper, we examine the problem of channel assignment in multi-hop wireless networks in which each node is equipped with multiple transceivers. We summarize various approaches to transceiver channel assignment. Most approaches in the literature seek to assign a channel to each available transceiver. In contrast, we seek to achieve a baseline connected topology that conserves resources, keeping some fraction of the transceivers inactive so as to dynamically react to changing traffic stimuli. We call our scheme resource-minimized channel assignment (RMCA). We show that RMCA achieves basic network connectivity and low interference, while using fewer resources compared to other protocols, and performs well under varied node density and number of transceivers per node. Finally, we evaluate several channel assignment schemes through simulation using a set of widely-accepted networking and graph-theoretic metrics.

This paper introduces two distributed algorithms for resource allocation in cellular networks with coexisting femtocells and macrocells. Network interference in these complex networks is modeled by a random graph to maintain the complexity of the examined networks' interference scenarios while abstracting away details such as node density and propagation characteristics. We propose two distributed algorithms for interference-free resource assignment: An uncoordinated algorithm designed for a case with no communications between base stations, and a coordinated algorithm designed to exploit information sharing between base stations. We assess the two algorithms by comparing their performance against a centralized heuristic algorithm and a centralized genetic algorithm, which together approximate the (computationally intractable) optimal allocation.

In this paper, a distributed efficient power allocation game-theoretic framework in wireless ad-hoc networks is proposed where multiple source nodes communicate with a single destination node via a relay node. The power allocation among the source nodes is formulated as an alternative ascending-clock auction (A-ACA) and achieved using a distributed algorithm that converges in a finite number of clocks and is proven to enforce truthful power demands at every clock and maximize the social welfare. Analytical and numerical results are presented to verify the efficient power allocation, truth-telling and social welfare maximization properties of the proposed A-ACA. It is concluded that the proposed A-ACA lends itself to practical implementation in wireless ad-hoc networks.

Simulation is still the most widely adopted performance evaluation technique in mobile ad hoc network research, in spite of a growing number of questions about the fidelity of this technique. Implementation-based testing and evaluation of wireless networks tends to produce believable results, but the technique sometimes suffers from poor repeatability, high implementation cost, and complex experimental logistics. The topic of this paper is software tools to enable implementation-based experimental research on wireless networks. We review some of the existing tools and propose the Flexible Internetwork Stack (FINS) framework, our open-source solution for network protocol implementation, integration, and testing. FINS aims to provide researchers with monitoring, logging, and reconfiguring utilities similar to the ones provided by simulation environments or emulation testbeds.1

Significant progress has been made in the past few years on Dynamic Spectrum Access (DSA) wireless networks which seek to use RF spectrum more efficiently and dynamically. For example, many measurements of current spectrum utilization are available and theoretical analyses and computational simulations of DSA networks abound. In sharp contrast, few network systems, particularly those with a decentralized structure, have been built even at a small scale to investigate the performance, behavior, and dynamics of DSA networks. Our contribution is designing a decentralized and asynchronous DSA network and building a prototype based on software radio technologies, signal detection and classification methods, distributed cooperative spectrum sensing systems, and mobile ad-hoc network (MANET) protocols. This paper details the network''s design and implementation as well as its enabling technologies. Through systematic experiments, we identify several factors influencing performance for decentralized DSA networks.

This paper studies downlink resource reuse schemes for cellular networks with coexisting femtocells and macrocells. We examine two reuse schemes, termed split reuse and shared reuse. In this paper, we develop an analytical model of resource allocation based on random graphs. In our model, arbitrarily chosen communication links interfere with each other with a certain probability, which depends upon whether the links belong to femtocell or macrocell users. Using this model, we establish asymptotic bounds on the minimum number of resource blocks required to make an interference-free resource assignment for all the users in the network for large numbers of users. We assess these bounds using a simple greedy resource allocation algorithm to demonstrate that the bounds are reasonable in finite networks of plausible size. By applying the bounds, we establish the expected impact of femtocell networks on macrocell resource allocation under a wide variety of interference scenarios. Further, we assess the efficiency loss associated with split reuse, as an aid to determining whether resource allocators should use the simpler split reuse scheme or attempt to tackle the complexity and overhead associated with shared reuse.

In a cognitive network, autonomous and adaptive radios select their operating parameters to achieve individual and network-wide goals. The effectiveness of these adaptations depends on the amount of knowledge about the state of the network that is available to the radios. We examine the price of ignorance in topology control in a cognitive network with power- and spectral-efficiency objectives. We propose distributed algorithms that, if radios possess global knowledge, minimize both the maximum transmit power and the spectral footprint of the network. We show that while local (as opposed to global) knowledge has little effect on the maximum transmission power used by the network, it has a significant effect on the spectral performance. Furthermore, we show that due to the high cost of maintaining network knowledge for highly dynamic networks, the cost/performance tradeoff makes it advantageous for radios to operate under some degree of local knowledge, rather than global knowledge.We also propose distributed algorithms for power and frequency adaptations as radios join or leave the network, and assess how partial knowledge impacts the performance of these adaptations.

We consider a problem where two selfish cognitive radio users try to share two channels on which they each have potentially different valuations. We first formulate the problem as a non-cooperative simultaneous game, and identify its equilibria. For cases where the resulting Nash equilibria are not efficient, we then propose a novel coordinated channel access mechanism that can be implemented with low overhead in a decentralized fashion. This mechanism, based on the Nash bargaining solution, guarantees full utilization of the spectrum resources while improving the utility of each user compared to the non-cooperative setting. We quantify the resulting gains. Finally, we prove that risk-averse users that are willing to accept offered information at face value have no incentive to lie to each other about their valuations for the non-cooperative game. However, we find that truthfulness is not guaranteed in the bargaining process, suggesting as an open problem the design of an incentive compatible mechanism for bargaining.

Typically, topology control is perceived as a per-node transmit power control process that achieves certain network- level objectives. We take an alternative approach of controlling the topology of a network purely by assigning channels to multiple radio interfaces on nodes. Specifically, we exploit the synergy between topology control and channel allocation to reduce the overall interference in multi-radio multi-channel wireless ad hoc networks. We formulate channel assignment as a non-cooperative game, with nodes selecting low interference channels while maintaining some degree of network connectivity. This game is shown to be a potential game, which ensures the existence of, and convergence to, a Nash equilibrium (NE). Next, we evaluate the performance of NE topologies with respect to interference and connectivity objectives. By quantifying the impact of channel availability on interference performance, we illuminate the tradeoff between interference reduction that can be achieved by distributing interference over multiple channels and the cost of having additional channels. Finally, we study the spectral occupancy of steady state topologies, and show that despite the non-cooperative behavior, the NE topologies achieve load balancing.

Multi-channel operation in an ad hoc network can improve robustness and reliability by efficiently managing interference and reducing contention. In this paper, we model four dynamic channel assignment techniques under the same set of assumptions, comparing the efficiency of their power and channel allocations. As the number of channels increases, the differences in the performance of the four techniques become more pronounced. Among the techniques studied, the conflict graph-based technique achieves the highest number of feasible links and the lowest average power consumption.

Topology control of ad hoc and mesh networks specifies how to assign per-node transmission parameters (such as power level, frequency etc.) so as to achieve energy efficiency, while maintaining certain desirable properties such as connectivity. In autonomous networks, nodes may act in their self- interest and improve their performance, perhaps at the expense of other nodes', or even the overall network's, performance. Besides, nodes must also contend with limited information about the network operating state during their decision-making. We analyze the above problem using non-cooperative game theory and quantify the impact of partial network state knowledge that nodes possess on the network optimality. We develop a local topology control algorithm that uses the idea of maintaining connectivity of 1-hop neighborhoods. This algorithm is first shown to converge and be stable. We then examine the trade-off between network performance (energy efficiency) and the cost of having knowledge (by exchanging control messages): more information exchange makes the nodes more network-aware, and hence leads to more efficient networks, but exchange of control information itself is costly. Taking the cost of obtaining knowledge into account, we observe that when nodes can operate along the continuum of knowledge, from 1-hop to omniscience, the network consumes least energy when nodes have significantly less connectivity information.

In this paper, we present the results of two simulation sets studying the effect of non-cooperation on the cognitive network performance. Simulations are conducted in the dynamic spectrum access ad-hoc cognitive network environment using the joint channel allocation-power control problem. The first simulations set studies the effect of mixing non-cognitive and cognitive nodes on the overall network performance while the second simulations set studies the effect of different selfish behaviors on the overall network performance. Simulation results and their analysis are provided.

In this paper, we investigate stochastic multichannel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Direct extensions of distributed greedy interference avoidance (IA) techniques developed for centralized networks to networks with multiple distributed receivers (as in ad hoc networks) are not guaranteed to converge. Motivated by this fact, we develop a waveform adaptation (WA) algorithm framework for IA based on potential game theory. The potential game model ensures the convergence of the designed algorithms in distributed networks and leads to desirable network solutions. Properties of the game model are then exploited to design distributed implementations of the algorithm that involve limited feedback in the network. Finally, variations of IA algorithms including IA with respect to legacy systems and IA with combined transmit-power and WA adaptations are investigated.

Various iterative algorithms for interference avoidance (IA) in networks with co-located receivers, suitable for distributed implementation, have been proposed in the literature. In this paper, the IA problem is cast in a game-theoretic framework and is formulated as a potential game. This formulation accommodates previously proposed algorithms and, in addition, gives us a framework that enables the design of new distributed and convergent algorithms for IA including algorithms with nonidentical utility functions for the users. Two new convergence results for potential games are then derived. The first result establishes the convergence of a class of potential games to the global solution while following best response iterations and when noise is added. The second result establishes the convergence of potential games to the Nash equilibria of the game while following random better response iterations. The first result combined with the potential game formulation allows us to show that for a large class of network scenarios, arbitrarily small noise assures the convergence of best response IA algorithms, including the eigeniterations, to an arbitrarily small neighborhood of the globally optimal signature sequence set. The second result enables the design of reduced feedback mechanisms for IA that converge to desirable solutions.

Recent studies on spectrum usage reveal poor utilization, both spatially and temporally. Opportunistic use of licensed spectrum while limiting interference to primary users can enhance spectrum reuse and provide orders of magnitude improvement in available channel capacity. This calls for spectrum sharing protocols that are dynamic, flexible, and efficient, in addition to being fair to end users. We employ cooperative game theory to address the opportunistic spectrum access problem. Specifically, we develop a game-theoretic model to analyze a scenario in which nodes in a wireless network seek to agree on a fair and efficient allocation of spectrum. First, we show that in high interference environments, the utility space of the game is non-convex, making certain optimal allocations unachievable with pure strategies. To mitigate this, we show that as the number of channels available increases, the utility space approaches convexity, thereby making optimal allocations achievable with pure strategies. Second, by comparing and analyzing three bargaining solutions, we show that the Nash bargaining solution achieves the best tradeoff between fairness and efficiency, using a small number of channels. Finally, we develop a distributed algorithm for spectrum sharing that is general enough to accomodate non-zero disagreement points, and show that it achieves allocations reasonably close to the Nash bargaining solution.

This paper describes an architecture for a node in a cognitive network that employs distributed learning and reasoning. We present the architecture and describe a method of distributed reasoning using an island genetic algorithm. We then formulate a channel allocation problem that is unique to the use of cognitive radio networks for dynamic spectrum access. We provide simulation results for the island genetic algorithm as applied to our channel allocation problem.

The problem of topology control is to assign per-node transmission power such that the resulting topology is energy efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a noncooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady-state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed, and the network performance is comparable to that obtained from centralized algorithms.

This paper presents a joint power control and waveform adaptation algorithm for networks with non-colocated receivers, amenable to a distributed implementation. The proposed algorithm allows users to meet their target signal to interference plus noise ratio (SINK) requirements while reducing the transmit power-levels in the network. The performance of the algorithm is investigated via theoretical-analysis and simulations. It is shown that the joint algorithm results in better solutions than a pure power-control or a pure waveform adaptation algorithm.

There is a need for new spectrum access protocols that are opportunistic, flexible and efficient, yet fair. Game theory provides a framework for analyzing spectrum access, a problem that involves complex distributed decisions by independent spectrum users. We develop a cooperative game theory model to analyze a scenario where nodes in a multi-hop wireless network need to agree on a fair allocation of spectrum. We show that in high interference environments, the utility space of the game is non-convex, which may make some optimal allocations unachievable with pure strategies. However, we show that as the number of channels available increases, the utility space becomes close to convex and thus optimal allocations become achievable with pure strategies. We propose the use of the Nash Bargaining Solution and show that it achieves a good compromise between fairness and efficiency, using a small number of channels. Finally, we propose a distributed algorithm for spectrum sharing and show that it achieves allocations reasonably close to the Nash Bargaining Solution.

Wireless topology control is the process of structuring the connectivity between network nodes to achieve some network-wide goal. This paper presents a cognitive network approach to achieving the objectives of power and spectrum management. We cast the problem as a two phased non-cooperative game and use the properties of potential game theory to ensure the existence of, and convergence to, a desirable Nash equilibrium. Although this is a multi-objective optimization and the spectrum management problem is NP-hard, this selfish cognitive network constructs a topology that minimizes the maximum transmission power while simultaneously using, on average, less than 12% extra spectrum, as compared to the ideal solution.

This paper examines the problem of topology con- trol in wireless ad-hoc networks. The purpose of topology control is to assign per-node optimal transmission power such that the resulting topology satisfies certain global properties such as connectivity. Due to the multi-hop nature of ad-hoc networks, establishing network connectivity may require nodes to use their power resources to service other nodes. Since nodes have limited power they may act selfishly in order to minimize their power (energy) consumption. Game theory is a suitable tool to analyze the conflicting objectives of nodes seeking to achieve an energy- efficient and connected network in the presence of selfish nodes. We present example topology control games and a distributed best response algorithm that together can guarantee convergence to steady state networks satisfying the dual topology control objectives. As a precursor, we analyze formation of a topology that results when nodes interact with each other. In particular, we provide a game theoretic framework for the topology control problem.

A framework to construct convergent interference avoidance (IA) algorithms in networks with multiple distributed receivers (as in ad hoc networks) based on potential game theory is developed in this paper. This is motivated by the fact that direct extensions of distributed greedy IA techniques for centralized networks to these de-centralized networks do not always lead to convergence. Some channel conditions that lead to non-convergence are also identified in the paper. A waveform adaptation algorithm for IA, designed on the basis of the framework, is then proposed. It is shown that this algorithm leads to a reduction of the interference in the network and also incorporates fairness in the allocation of resources.

We show that the fixed power, synchronous interference avoidance (IA) scheme of (C. Rose et al, IEEE Trans. on Wireless Comm., vol.1, no.3, p. 415-427, 2002) employing the (greedy) eigen-iteration can be modeled as the recently developed potential game of (D. Monderer et al, Journal of Games and Economic Behavior, vol.14, no.0044, p.124-143, 1996). Motivated by the fact that receivers can make small mistakes, we consider the convergence of the eigen-iteration when noise is added in a manner similar to (P. Anigstein, IEEE Trans. On Inf. Theory vol.49, no.4, 2003). Further, we restrict ourselves to a class of signal environments that we call levelable environments. Applying game-theory, we obtain a convergence result similar to that of the Anigstein method, for levelable environments: arbitrarily small noise assures that the eigen-iteration almost surely converges to a neighborhood of the optimum signature set.

Aloha is perhaps the simplest and most-studied medium access control protocol in existence. Only in the recent past, however, have researchers begun to study the performance of Aloha in the presence of selfish users. In this paper, we present a game-theoretic model of multipacket slotted Aloha with perfect information. We show that this model must have an equilibrium and we characterize this equilibrium. Using the tools of stochastic processes, we then establish the equilibrium stability region for some well-known channel models.

Game theory is a set of tools developed to model interactions between agents with conflicting interests, and is thus well-suited to address some problems in communications systems. We present some of the basic concepts of game theory and show why it is an appropriate tool for analyzing some communication problems and providing insights into how communication systems should be designed. We then provided a detailed example in which game theory is applied to the power control problem in a CDMA-like system.

Past studies of Aloha have emphasized system-wide goals such as achieving maximum throughput or minimum delay. We use game theory to analyze Aloha from the perspective of a selfish user. we construct an Aloha game and examine the optimal behavior of individual users. We show that the Aloha game has an equilibrium and that an Aloha system in which the users are selfish will be stable provided the attempt rate is sufficiently low. We then compare the performance of a selfish Aloha system with the performance of a centrally controlled slotted Aloha system. With some system parameters performance is near the optimum performance obtained by a centrally-controlled system. By utilizing a selfish-user assumption, it is possible to build systems which are robust and scalable.