138,167 research outputs found
Harmonized Cellular and Distributed Massive MIMO: Load Balancing and Scheduling
Multi-tier networks with large-array base stations (BSs) that are able to
operate in the "massive MIMO" regime are envisioned to play a key role in
meeting the exploding wireless traffic demands. Operated over small cells with
reciprocity-based training, massive MIMO promises large spectral efficiencies
per unit area with low overheads. Also, near-optimal user-BS association and
resource allocation are possible in cellular massive MIMO HetNets using simple
admission control mechanisms and rudimentary BS schedulers, since scheduled
user rates can be predicted a priori with massive MIMO.
Reciprocity-based training naturally enables coordinated multi-point
transmission (CoMP), as each uplink pilot inherently trains antenna arrays at
all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP,
which improves cell-edge performance without requiring channel state
information exchanges among cooperating BSs. We present methods for harmonized
operation of distributed and cellular massive MIMO in the downlink that
optimize resource allocation at a coarser time scale across the network. We
also present scheduling policies at the resource block level which target
approaching the optimal allocations. Simulations reveal that the proposed
methods can significantly outperform the network-optimized cellular-only
massive MIMO operation (i.e., operation without CoMP), especially at the cell
edge
Distributed and Centralized Task Allocation: When and Where to Use Them
Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms
Non-convex resource allocation in communication networks
The continuously growing number of applications competing for resources
in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization
Theory can provide the necessary tools to develop such mechanisms that will
allocate network resources optimally and fairly among users. However, the
resource allocation problem in current networks has characteristics that turn
the respective optimization problem into a non-convex one. First, current
networks very often consist of a number of wireless links, whose capacity is
not constant but follows Shannon capacity formula, which is a non-convex
function. Second, the majority of the traffic in current networks is generated
by multimedia applications, which are non-concave functions of rate. Third,
current resource allocation methods follow the (bandwidth) proportional
fairness policy, which when applied to networks shared by both concave
and non-concave utilities leads to unfair resource allocations. These characteristics make current convex optimization frameworks inefficient in several
aspects. This work aims to develop a non-convex optimization framework
that will be able to allocate resources efficiently for non-convex resource allocation formulations. Towards this goal, a necessary and sufficient condition
for the convergence of any primal-dual optimization algorithm to the optimal solution is proven. The wide applicability of this condition makes this a fundamental contribution for Optimization Theory in general. A number
of optimization formulations are proposed, cases where this condition is not
met are analysed and efficient alternative heuristics are provided to handle
these cases. Furthermore, a novel multi-sigmoidal utility shape is proposed
to model user satisfaction for multi-tiered multimedia applications more accurately. The advantages of such non-convex utilities and their effect in the
optimization process are thoroughly examined. Alternative allocation policies are also investigated with respect to their ability to allocate resources
fairly and deal with the non-convexity of the resource allocation problem. Specifically, the advantages of using Utility Proportional Fairness as an allocation policy are examined with respect to the development of distributed
algorithms, their convergence to the optimal solution and their ability to
adapt to the Quality of Service requirements of each application
Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service
With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798
Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers
This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns
Incentive-compatible route coordination of crowdsourced resources
Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in
which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a
mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments
- …