We propose an algorithm for opportunistic multicasting in wireless networks. Whereas prior multicast rate adaptation schemes primarily optimize long-term throughput, we investigate the finite horizon problem where a fixed number of packets has to be transmitted to a set of wireless receivers in the shortest amount of time-a common problem, e.g., for software updates or video multicast. In the finite horizon problem, the optimum rate critically depends on the recent reception history of the receivers and requires a fine balance between maximizing overall throughput and equalizing individual receiver throughput. We formulate a dynamic programming algorithm that optimally solves this problem. We then develop two low complexity heuristics that perform close to the optimal solution and are suitable for practical online scheduling. We further analyze the performance of our algorithms by means of simulation. They substantially outperform existing solutions based on throughput maximization or favoring the user with the worst channel, and we obtain a 30 percent performance improvement over the former and a 120 percent improvement over the latter in scenarios with Rayleigh fading. We further analyze the performance of the schemes under imperfect state information and observe an even higher improvement over the benchmark schemes.

Current IEEE 802.11 WLANs suffer from the well-known rate anomaly problem, which can drastically reduce network performance. Opportunistic relaying can address this problem, but three major considerations, typically considered separately by prior work, need to be taken into account for an efficient deployment in real-world systems: 1) relaying could imply increased power consumption, and nodes might be heterogeneous, both in power source (e.g., battery-powered versus socket-powered) and power consumption profile; 2) similarly, nodes in the network are expected to have heterogeneous throughput needs and preferences in terms of the throughput versus energy consumption tradeoff; and 3) any proposed solution should be backwards-compatible, given the large number of legacy 802.11 devices already present in existing networks. In this paper, we propose a novel framework, Self-Optimizing, Legacy-Compatible Opportunistic Relaying (SOLOR), which jointly takes into account the above considerations and greatly improves network performance even in systems comprised mostly of vanilla nodes and legacy access points. SOLOR jointly optimizes the topology of the network, i.e., which are the nodes associated to each relay-capable node; and the relay schedules, i.e., how the relays split time between the downstream nodes they relay for and the upstream flow to access points. Our results, obtained for a large variety of scenarios and different node preferences, illustrate the significant gains achieved by our approach. Specifically, SOLOR greatly improves network throughput performance (more than doubling it) and power consumption (up to 75% reduction) even in systems comprised mostly of vanilla nodes and legacy access points. Its feasibility is demonstrated through testbed experimentation in a realistic deployment.

Wireless multicasting suffers from the problem that the transmit rate is usually determined by the receiver with the worst channel. Composite or adaptive beamforming allows using beamforming patterns that trade off antenna gains between receivers. A common solution for wireless multicast with beamforming is to select the pattern that maximizes the minimum rate among all receivers (for a given transmit power). However, when using opportunistic multicast to transmit a finite number of packets to all receivers - the finite horizon problem - this is no longer optimal. Instead, the optimum beamforming pattern depends on instantaneous channel conditions as well as the number of received packets at each receiver. We formulate the finite horizon multicast beamforming problem as a dynamic programming problem to obtain the optimal solution. We further design a heuristic that has sufficiently low complexity to be implementable in practice and show through extensive simulations that our algorithm significantly outperforms prior solutions.

Dense deployment of small cells is an important, emerging trend to enable future cellular networks to cope with growing traffic demand. However, this reduces the number of users per cell and thus opportunistic scheduling gain. We propose a base station-driven energy-aware approach to exploit user-user communication to increase the opportunistic gain. We use tools from stochastic Lyapunov optimization to determine the optimal scheduling policy subject to a constraint on energy consumption for user-user communication. Our simulation results show that with a large energy budget, packet transfer delay is reduced by up to 70%. The bulk of the performance improvement can be achieved with only a small increase in energy consumption, where 60% of the improvement is achieved at only 20% of the additional energy consumption. Further, we evaluate our algorithm using realistic video traffic traces and show that frame loss ratio is reduced by 90% and PSNR is improved by 4dB.

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. The number of users in each cell is thus reduced, which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose a user-initiated base station (BS)-transparent traffic spreading approach that leverages user-user communication to increase BS scheduling flexibility. The proposed scheme can increase opportunistic gain and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

Context-awareness is a peculiar characteristic of an ever expanding set of applications that make use of a combination of restricted spatio-temporal locality and mobile communications, to deliver a variety of services to the end user. It is expected that by 2014 more than 1.5 billion people would be using applications based on local search (search restricted on the basis of spatio-temporal locality), and that mobile location based services will drive revenues of more than $15 billion worldwide. A common feature of such context-aware applications is the fact that their communication requirements significantly differ from ordinary applications. For most of them, the scope of generated content itself is local. This locally relevant content may be of little concern to the rest of the world, therefore moving this content from the user device to store it in a well-accessible centralized location and/or making this information available beyond its scope represents a clear waste of resources (connectivity, storage). Due to these specific requirements, opportunistic communication can play a special role when coupled with context-awareness. The benefit of opportunistic communications is that it naturally incorporates context as spatial proximity is closely associated with connectivity.

Context-awareness is a peculiar characteristic of an expanding set of applications that make use of a combination of restricted spatio-temporal locality and mobile communications, to deliver a variety of services. Opportunistic communications satisfy well the communication requirements of these applications, because they naturally incorporate context. Recently, an opportunistic communication paradigm called "Floating Content" (FC) was proposed, to support infrastructure-less, distributed content sharing. But how good is floating content in supporting context-aware applications? In this work, we present a simple approximate analytical model for the performance analysis of context-aware applications that use floating content. We estimate the "success probability" for a representative category of context-aware applications, and show how the system can be configured to achieve the application's target QoS. We validate our model using extensive simulations under different settings and mobility patterns, showing that our model-based predictions are highly accurate under a wide range of conditions.

We consider the problem of minimizing the energy consumed in a cellular access network, under loads that slowly vary over space and time, while guaranteeing quality of service (QoS). In particular, we formalize the problem of jointly optimizing the base stations (BS) power levels and the association of users to BSs, while guaranteeing a minimum throughput to each user, and a target value of blocking probability. We propose abstractions that enable tracking of long-term spatial load distributions, and a practical algorithm for energy efficient user association and base station power allocation. Our algorithm is applicable to arbitrary (planar) BS layouts, to settings with interference, to different BS energy models, and to arbitrary user distributions over the service area. Through extensive simulations using measured data, and realistic BS deployments, we show that our algorithm leads to substantial energy savings both with traditional BS designs and with energy-proportional equipment, and we demonstrate the potential of BS sleep modes to achieve network-level energy proportionality.

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. Thus, the number of concurrent users served by each base station (BS) is likely to be small which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose user-initiated BS-transparent traffic spreading that leverages user-to-user communication to increase BS scheduling flexibility. The proposed scheme is able to increase opportunistic gains and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios, and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

In explicit or delay-driven congestion control, a common objective is to sustain high throughput without long queues and large losses at the bottleneck link of the network path. Congestion control protocols strive to achieve this goal by transmitting smoothly in the steady state. The discovery of the appropriate steady-state transmission rates is a challenging task in itself and typically introduces additional queuing and losses. Seeking insights into the steady-state profiles of queuing and loss achievable by real protocols, this paper presents an AIST (Asynchronous arrivals with Ideally Smooth Transmission) model that abstracts away transient queuing and losses related to discovering the path capacity and redistributing it fairly among the packet flows on the bottleneck link. In AIST, the flows arrive asynchronously but transmit their packets at the same constant rate in the steady state. For the link with an overprovisioned buffer, our queuing-theoretic analysis and simulations for different smooth distributions of packet interarrival times agree that queuing under AIST with the target utilization of 1 is on the order of the square root of N, where N is the number of flows. With small buffers, our simulations of AIST show an ability to provide bounded loss rates regardless of the number of flows.

This paper presents a system-level approach to interference management in an infrastructure-based wireless network with full frequency reuse. The key idea is to use loose base-station coordination that is tailored to the spatial load distribution and the propagation environment to exploit the diversity in a user population's sensitivity to interference. System architecture and abstractions to enable such coordination are developed for both the downlink and the uplink cases, which present differing interference characteristics. The basis for the approach is clustering and aggregation of traffic loads into classes of users with similar interference sensitivities that enable coarse-grained information exchange among base stations with greatly reduced communication overheads. This paper explores ways to model and optimize the system under dynamic traffic loads where users come and go, resulting in interference-induced performance coupling across base stations. Based on extensive system-level simulations, we demonstrate load-dependent reductions in file transfer delay ranging from 20%-80% as compared to a simple baseline not unlike systems used in the field today while simultaneously providing more uniform coverage. Average savings in user power consumption of up to 75% is achieved. Performance results under heterogeneous spatial loads illustrate the importance of being traffic- and environment-aware.

We study the impact of user association policies on flow-level performance in interference-limited wireless networks. Most research in this area has used static interference models (neighboring base stations are always active) and resorted to intuitive objectives such as load balancing. In this paper, we show that this can be counterproductive in the presence of dynamic interference that couples the transmission rates to users at various base stations. We propose a methodology to optimize the performance of a class of coupled systems and apply it to study the user association problem. We show that by properly inducing load asymmetries, substantial performance gains can be achieved relative to a load-balancing policy (e.g., 15 times reduction in mean delay). We present a practical, measurement based, interference-aware association policy that infers the degree of interference-induced coupling and adapts to it. Systematic simulations establish that both our optimized static and adaptive association policies substantially outperform various dynamic policies that can, in extreme cases, even be susceptible to Braess's paradox-like phenomena, i.e., an increase in the number of base stations can lead to worse performance under greedy association policies. Furthermore, these results are robust to changes in file-size distributions, large-scale propagation parameters, and spatial load distributions.

Two ways to substantially enhance wireless broadband capacity are full frequency reuse and smaller cells, both of which result in operational regimes that are highly dynamic and interference limited. This paper presents a system-level approach to interference management, that has reasonable backhaul communication and computation requirements. The basis for the approach is clustering and aggregation of measurements of the spatial diversity in sensitivity to interference associated with average user populations. This enables the system to exchange information and optimize coordinated transmission schedules using only coarse grained data. The paper explores various ways of optimizing such schedules: from a static, decoupled version to a dynamic version capturing user-level scheduling, fluctuating loads and inter-cell interference that couples base stations' performance. Based on extensive system-level simulations, we demonstrate reductions in file transfer delay ranging from 20-80%, from light to heavy loads, as compared to a simple baseline not unlike those in the field today. This improvement is achieved while providing more uniform coverage, and reducing base station power consumption by up to 45%.