Massive MIMO with Optimal Power and Training Duration Allocation

We consider the uplink of massive multicell multiple-input multiple-output systems, where the base stations (BSs), equipped with massive arrays, serve simultaneously several terminals in the same frequency band. We assume that the BS estimates the channel from uplink training, and then uses the maximum ratio combining technique to detect the signals transmitted from all terminals in its own cell. We propose an optimal resource allocation scheme which jointly selects the training duration, training signal power, and data signal power in order to maximize the sum spectral efficiency, for a given total energy budget spent in a coherence interval. Numerical results verify the benefits of the optimal resource allocation scheme. Furthermore, we show that more training signal power should be used at low signal-to-noise ratio (SNRs), and vice versa at high SNRs. Interestingly, for the entire SNR regime, the optimal training duration is equal to the number of terminals.

A new method for transmission loss allocation considering the circulating currents between generators

A new method is presented for transmission loss allocation based on the separation of transmission loss caused by load and the loss due to circulating currents between generators. The theoretical basis for and derivation of the loss formulae are presented using simple systems. The concept is then extended to a general power system using the Ybus model. Details of the application of the proposed method to a typical power system are presented along with results from the IEEE 30 bus test system. The results from both the small system and the standard IEEE test system demonstrate the validity of the proposed method.

Distributed Region-Based Memory Allocation and Synchronization

We present DRASync, a region-based allocator that implements a global address space abstraction for MPI programs with pointer-based data structures. The main features of DRASync are: (a) it amortizes communication among nodes to allow efficient parallel allocation in a global address space; (b) it takes advantage of bulk deallocation and good locality with pointer-based data structures; (c) it supports ownership semantics of regions by nodes akin to reader–writer locks, which makes for a high-level, intuitive synchronization tool in MPI programs, without sacrificing message-passing performance. We evaluate DRASync against a state-of-the-art distributed allocator and find that it produces comparable performance while offering a higher-level abstraction to programmers.

Spatial allocation of material flow analysis in residential developments: a case study of Kildare County, Ireland

Studies of urban metabolism provide important insights for environmental management of cities, but are not widely used in planning practice due to a mismatch of data scale and coverage. This paper introduces the Spatial Allocation of Material Flow Analysis (SAMFA) model as a potential decision support tool aimed as a contribution to overcome some of these difficulties and describes its pilot use at the county level in the Republic of Ireland. The results suggest that SAMFA is capable of identifying hotspots of higher material and energy use to support targeted planning initiatives, while its ability to visualise different policy scenarios supports more effective multi-stakeholder engagement. The paper evaluates this pilot use and sets out how this model can act as an analytical platform for the industrial ecology–spatial planning nexus.

Energy-Aware Rate and Description Allocation Optimized Video Streaming for Mobile D2D Communications

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

Multimedia Resource Allocation in mmWave 5G Networks

The 5G network infrastructure is driven by the evolution of today's most demanding applications. Already, multimedia applications such as on-demand HD video and IPTV require gigabit- per-second throughput and low delay, while future technologies include ultra HDTV and machine-to-machine communication. Mm-Wave technologies such as IEEE 802.15.3c and IEEE 802.11ad are ideal candidates to deliver high throughput to multiple users demanding differentiated QoS. Optimization is often used as a methodology to meet throughput and delay constraints. However, traditional optimization techniques are not suited to a mixed set of multimedia applications. Particle swarm optimization (PSO) is shown as a promising technique in this context. Channel-time allocation PSO (CTA-PSO) is successfully shown here to allocate resource even in scenarios where blockage of the 60 GHz signal poses significant challenges.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

Uncertainty in the Cloud: An Angel-Daemon Approach to Modelling Performance

Uncertainty profiles are used to study the effects of contention within cloud and service-based environments. An uncertainty profile provides a qualitative description of an environment whose quality of service (QoS) may fluctuate unpredictably. Uncertain environments are modelled by strategic games with two agents; a daemon is used to represent overload and high resource contention; an angel is used to represent an idealised resource allocation situation with no underlying contention. Assessments of uncertainty profiles are useful in two ways: firstly, they provide a broad understanding of how environmental stress can effect an application’s performance (and reliability); secondly, they allow the effects of introducing redundancy into a computation to be assessed

Partial Cooperation in Strategic Decision Situations

We consider a normal form game in which there is a single exogenously given coalition of cooperating players that can write a binding agreement on pre-selected actions. These collective actions typically represent a certain number of dimensions in the players’ strategy space. The actions represented by the other dimensions of the strategy space remain under the complete, individual control of the players.
We consider a standard extension of the Nash equilibrium concept denoted as a partial cooperative equilibrium as well as an equilibrium concept in which the coalition of cooperators has a leadership position. Existence results are developed for these new equilibrium concepts. We identify conditions on these partial cooperative games under which the various equilibrium concepts are equivalent.
We apply this game theoretic framework to existing models of multi-market oligopolies and international pollution abatement. In a multi-market oligopoly typically a merger paradox emerges in the partial cooperative equilibrium, which vanishes if the cartel of collaborators exploits its leadership position. Our application to international pollution abatement treaties shows that cooperation by a sufficiently large group of countries results in a Pareto improvement over the standard tragedy of the commons outcome described by the Nash equilibrium.