Data Centers (DCs) are required to be scalable to large data sets so as to accommodate ever increasing demands of resource-limited embedded and mobile devices. Thanks to the availability of recent high data rate millimeter-wave frequency spectrum such as 60GHz and due to the favorable attributes of this technology, wireless DC (WDC) exhibits the potentials of being a promising solution especially for small to medium scale DCs. This paper investigates the problem of throughput scalability of WDCs using the established theory of the asymptotic throughput of wireless multi-hop networks that are primarily proposed for homogeneous traffic conditions. The rate-heterogeneous traffic distribution of a data center however, requires the asymptotic heterogeneous throughput knowledge of a wireless network in order to study the performance and feasibility of WDCs for practical purposes. To answer these questions this paper presents a lower bound for the throughput scalability of a multi-hop rate-heterogeneous network when traffic generation rates of all nodes are similar, except one node. We demonstrate that the throughput scalability of conventional multi-hopping and the spatial reuse of the above bi-rate network is inefficient and henceforth develop a speculative 2-partitioning scheme that improves the network throughput scaling potentials. A better lower bound of the throughput is then obtained. Finally, we obtain the throughput scaling of an i.i.d. rate-heterogeneous network and obtain its lower bound. Again we propose a speculative 2-partitioning scheme to achieve a network with higher throughput in terms of improved lower bound. All of the obtained results have been verified using simulation experiments.
What has been mentioned so far, is achieved by the assumption of no cooperation among nodes. Xie and Kumar in @cite_20 investigated the strategy of multi-hop wireless networks with nodes cooperation. " O zg " u in @cite_26 , proposed an order-optimal scheme, with help of the distributed MIMO technique.
@cite_26 @cite_20
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This paper explores the usage of cooperative multiple input multiple output (MIMO) technique to minimize energy consumption used to establish communications among distant nodes in a wireless sensor network (WSN). As energy depletion is an outstanding problem in WSN research field, a number of techniques aim to preserve such resource, especially by means of savings during communication among sensor nodes. One such wide used technique is multi-hop communication to diminish the energy required by a single node to transmit a given message, providing a homogeneous consumption of the energy resources among the nodes in the network. However, it is not the case that multi-hop is always more efficient than single-hop, even that it may represent a great depletion of a single node’s energy. In this paper a cooperative MIMO transmission technique for WSN is presented, which is compared to single-hop and multi-hop transmission ones, highlighting its advantages in relation to both. Simulation results support the statement about the utility in applying the proposed technique for energy saving purposes. This paper considers the problem of finding minimum-energy cooperative routes in a wireless network with variable wireless channels. We assume that each node in the network is equipped with a single omnidirectional antenna and, motivated by the large body of physical layer research indicating its potential utility, that multiple nodes are able to coordinate their transmissions at the physical layer in order to take advantage of spatial diversity. Such coordination, however, is intrinsically intertwined with routing decisions, thus motivating the work. We first formulate the energy cost of forming a cooperative link between two nodes based on a two-stage transmission strategy assuming that only statistical knowledge about channels is available. Utilizing the link cost formulation, we show that optimal static routes in a network can be computed by running Dijkstra's algorithm over an extended network graph created by cooperative links. However, due to the variability of wireless channels, we argue that a many-to-one cooperation model in static routing is suboptimal. Hence, we develop an opportunistic routing algorithm based on many-to-many cooperation, and show that optimal routes in a network can be computed by a stochastic version of the Bellman-Ford algorithm. We use static and opportunistic optimal algorithms as baselines to develop heuristic link selection algorithms that are energy efficient while being computationally simpler than the optimal algorithms. We simulate our algorithms and show that while optimal cooperation and link selection can reduce energy consumption by almost an order of magnitude compared to non-cooperative approaches, our simple heuristics achieve similar energy savings while being computationally efficient as well. Large multiple-input multiple-output (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge overhead is required to obtain full CSI especially for Frequency-Division Duplex (FDD) systems. To reduce overhead, we propose a downlink antenna selection scheme, which selects S antennas from M > S transmit antennas based on the large scale fading to serve K ≤ S users in large distributed MIMO networks employing regularized zero-forcing (RZF) precoding. In particular, we study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. This is a mixed combinatorial and non-convex problem whose objective and constraints have no closed-form expressions. We apply random matrix theory to derive asymptotically accurate expressions for the objective and constraints. As such, the joint optimization problem is decomposed into subproblems, each of which is solved by an efficient algorithm. In addition, we derive structural solutions for some special cases and show that the capacity of very large distributed MIMO networks scales as O(KlogM) when M→∞ with K, S fixed. Simulations show that the proposed scheme achieves significant performance gain over various baselines. This paper explores the idea of cooperative spatial multiplexing for use in MIMO multicell networks. We imagine applying this cooperation for several multiple antenna access-points to jointly transmit streams towards multiple single-antenna user terminals in neighbouring cells. We make the setting more realistic by introducing a constraint on the hybrid channel state information (HCSI), assuming that each transmitter has full CSI for its own channel, but only statistical information about other transmitters’ channels. Each cooperating transmitter then makes guesses about the behaviour of the other transmitters, using the statistical CSI. We show two of several possible transmission strategies under this setting, and include simple optimization at the receiver to improve performance. Comparisons are made with fully cooperative (full CSI) and non-cooperative schemes. Simulation results show a substantial cooperation gain despite the lack of instantaneous information.