Exploring knowledge management integration through EAI in local government domain

Information Technology (IT) infrastructure integration and knowledge management share communal objectives e.g. to make organisations more effective and efficient, agile and innovative, and more responsive to market changes. Such association when assimilates bona fide knowledge management philosophy, it offers the IT departments a headship opportunity for organisational transformation in affiliation with the rest of the establishment. Notwithstanding, in the context of Local Government Authorities (LGAs), the non-integrated nature of the IT infrastructure is associated with inefficient data and knowledge exchange and reduction in LGA services’ quality. Therefore, numerous data inconsistencies and redundancies occur that impact LGA services to their citizenry. LGAs have deployed Enterprise Application Integration (EAI) technological solutions to integrate their legacy with new developed Information Systems (IS). Literature indicates that EAI achieves integration at five layers namely: (a) connectivity, (b) transportation, (c) transformation, (d) process integration and (e) knowledge integration. This research adapts a Revised Model for Integration Layers (REAL) and tests through a case study in a local authority. The results indicate that cases leading to data inconsistencies and replication can be prevented by integrating knowledge through EAI

Optimal Power Control in Decentralized Gaussian Multiple Access Channels

We consider the decentralized power optimization problem for Gaussian fast-fading multiple access channel (MAC) so that the average sum-throughput is maximized. In our MAC setup, each transmitter has access to only its own fading coefficient or channel state information (CSI) while the receiver has full CSI available at all instants. Unlike centralized MAC (full CSIT MAC) where the optimal powers are known explicitly, the analytical solution for optimal decentralized powers does not seem feasible. In this letter, we specialize alternating-maximization (AM) method for numerically computing the optimal powers and ergodic capacity of the decentralized MAC for general fading statistics and average power constraints. For illustration, we apply our AM method to compute the capacity of MAC channels with fading distributions such as Rayleigh, Rician etc.Comment: 4 pages, 4 figures, accepted for publication to IEEE Communication letter