The metal-to-ligand charge-transfer (MLCT) excited states of Ru(bpy)(2)(deeb)(PF(6))(2), where bpy is 2,2-bipyridine and deeb is 4,4'-(CO(2)CH(2)CH(3))(2)-2,2'-bipyridine, in dichloromethane were found to be efficiently quenched by iodide at room temperature. The ionic strength dependence of the UV-visible absorption spectra gave evidence for ion pairing. Iodide was found to quench the excited states by static and dynamic mechanisms. Stern-Volmer and Benesi-Hildebrand analysis of the spectral data provided a self-consistent estimate of the iodide-Ru(bpy)(2)(deeb)(2+) adduct in dichloromethane, K = 59 700 M(-1). Transient absorption studies clearly demonstrated an electron-transfer quenching mechanism with transient formation of I(2)(*)(-) in high yield, phi = 0.25 for 355 or 532 nm excitation. For Ru(bpy)(2)(deeb)(PF(6))(2) in acetonitrile, similar behavior could be observed at higher iodide concentrations than that required in dichloromethane. The parent Ru(bpy)(3)(2+) compound also ion pairs with iodide in CH(2)Cl(2), and light excitation gave a higher I(2)(*)(-) yield, phi = 0.50. X-ray crystallographic, IR, and Raman data gave evidence for interactions between iodide and the coordinated deeb ligand in the solid state.

A theory of the distributed detection of radar targets is developed. The radar system employs a number of physically separated peripheral receivers and detectors and a central processor that provides a final decision by combining peripheral decisions rather than decision statistics. Various combining strategies are studied in the context of overall system optimality. It is shown that the optimal decision strategy of a peripheral detector depends on the strategies of the other peripheral detectors and, interestingly, on the structure of the central processor. To illustrate application of the theory, the design of a distributed system in a Rayleigh fading environment is studied.

The excited states of [Ru(bpy)2(deeb)](PF6)2, where bpy is 2,2-bipyridine and deeb is 4,4'-(CO2CH2CH3)2-2,2'-bipyridine, were found to be efficiently quenched by triiodide (I3-) in acetonitrile and dichloromethane. In dichloromethane, I3- was found to quench the excited states by static and dynamic mechanisms; Stern-Volmer analysis of the time-resolved and steady-state photoluminescence data produced self-consistent estimates for the I3- + Ru(bpy)2(deeb)2+ <==> [Ru(II)(bpy)2(deeb)2+,(I3-)]+ equilibrium, K = 51,000 M(-1), and the bimolecular quenching rate constant, kq = 4.0 x 10(10) M(-1) s(-1). In acetonitrile, there was no evidence for ion pairing and a dynamic quenching rate constant of k(q) = 4.7 x 10(10) M(-1) s(-1) was calculated. Comparative studies with Ru(bpy)2(deeb)2+ anchored to mesoporous nanocrystalline TiO2 thin films also showed efficient excited-state dynamic quenching by I3- in both acetonitrile and dichloromethane, kq = 1.8 x 10(9) and 3.6 x 10(10) M(-1) s(-1), respectively. No reaction products for the excited-state quenching processes were observed by nanosecond transient absorption measurements from 350 to 800 nm under any experimental conditions. X-ray crystallographic, IR, and Raman data gave evidence for interactions between I3- and the bpy and deeb ligands in the solid state.

Abstract-A MIMO network is a wireless network made up of individual MIMO links. The problem we consider is to maximize throughput in a multihop MIMO network with interference suppression. Our problem formulation accounts for variable rates on the MIMO links, which depend on the channel conditions of the link, and the manner in which the diversity-multiplexing trade-off is handled. We present an ILP formulation of the MIMO one-shot scheduling problem with variable rates, which is the first exact formulation of a MIMO network optimization problem that accounts for full interference suppression capabilities of MIMO links. We use CPLEX to evaluate the optimal solution based on the ILP formulation for wireless networks with up to 32 concurrently transmitting links. We also modify a heuristic algorithm from a related MIMO scheduling problem to work in our problem setting. Results show that the heuristic can scale to networks with 80 or more concurrent links, but is 10-20% from optimal in terms of throughput. We show that the heuristic scheduler is not able to fully exploit the diversitymultiplexing-interference suppression tradeoff, which is inherent in the problem. This shows that there is substantial room for developing improved scheduling algorithms for MIMO networks and provides some insight into promising directions to explore.

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