QoS Optimization Problem Research Articles

Comment on “Optimal Precoding for a QoS Optimization Problem in Two-User MISO-NOMA Downlink”

Recently, optimum non-orthogonal multiple access (NOMA) precoding for a two-user multiple-input single-output (MISO) downlink has been proposed by Chen et al. Reference [1, Proposition 1] demonstrates that strong duality holds for the MISO-NOMA precoding optimization problem. Hence, the proposed preocoding algorithm is not only locally optimal, but also globally optimal. However, the proof of this proposition is flawed. In this regard, we provide a corrected proof in this comment.

MPSO Algorithm Based QoS Parameter Optimization for LTE Networks

QoS Optimization is an important part of LTE SON, but not yet defined in the specification. We discuss modeling the problem of QoS optimization, improve the fitness function, then provide an algorithm based on MPSO to search the optimal QoS parameter value set for LTE networks. Simulation results show that the algorithm converges more quickly and more accurately than the GA which can be applied in LTE SON.

Optimal Precoding for a QoS Optimization Problem in Two-User MISO-NOMA Downlink

In this letter, based on the non-orthogonal multiple access (NOMA) concept, a quality-of-service optimization problem for two-user multiple-input-single-output broadcast systems is considered, given a pair of target interference levels. The minimal power and the optimal precoding vectors are obtained by considering its Lagrange dual problem and via Newton’s iterative algorithm, respectively. Moreover, the closed-form expressions of the minimal transmission power for some special cases are also derived. One of these cases is termed quasi-degraded, which is the key point and will be discussed in detail in this letter. Our analysis further figures out that the proposed NOMA scheme can approach nearly the same performance as optimal dirty paper coding, as verified by computer simulations.

Efficiently Compositing and Optimizing the Quality of Heterogeneous Services

The emerging service-oriented computing bring various services together to a ‘market' for clients to request, enabling the integration of services across the distributed, heterogeneous, and wide area networks. By encapsulating diverse implementations of physical and software resources in the interface that standardizes the functions, loosely coupled services have show great potential to support a versatile integration platform for complex applications. Existing techniques can successfully support integrating services from different providers to provide applications with more complex user requirements. However, to automatically composite services with guaranteed QoS is not trivial. In this paper, the authors propose Q-SAC, a QoS optimized service automatic composition model. Two main features of Q-SAC include automatic service composition, and multidimensional QoS optimization for the composition plan. The authors show that the problem of Q-SAC QoS optimization is NP-hard, and design a heuristic simulated annealing algorithm. Through simulation the authors show that this design is practical and efficient.

VOIP Call Optimization in Diverse Network Scenarios Using Learning Based State-Space Search Technique

A VoIP based call has stringent QoS requirements with respect to delay, jitter, loss, MOS and R-Factor. Various QoS mechanisms implemented to satisfy these requirements must be adaptive under diverse network scenarios and applied in proper sequence, otherwise they may conflict with each other. The objective of this paper is to address the problem of adaptive QoS maintenance and sequential execution of available QoS implementation mechanisms with respect to VoIP under varying network conditions. In this paper, we generalize this problem as state-space problem and solve it. Firstly, we map the problem of QoS optimization into state-space domain and apply incremental heuristic search. We implement the proposed algorithm under various network and user scenarios in a VoIP test-bed for QoS enhancement. Then learning strategy is implemented for refinement of knowledge base to improve the performance of call quality over time. Finally, we discuss the advantages and uniqueness of our approach.

Towards security and QoS optimization in real-time embedded systems

A number of real-time embedded systems (RTESs) are used to manage critical infrastructure such as electric grids or C 4 I systems. In these systems, it is essential to meet deadlines, for example, to avoid a power outage or loss of a life. The importance of security support is also increasing, because more RTESs are being networked. To securely transmit sensitive data, e.g., a battle field status, across the network, RTESs need to protect the data via cryptographic techniques. However, security support may cause deadline misses or unacceptable QoS degradation. As an initial effort to address this problem, we formulate the security support in RTESs as a QoS optimization problem. Also, we propose a novel adaptive approach for security support in which a RTES initially uses a relatively short cryptographic key to maximize the QoS, while increasing the key length when the security risk level is raised. In this way, we can make a possible cryptanalysis several orders of magnitude harder by requiring the attacker to search a larger key space, while meeting all deadlines by degrading the QoS in a controlled manner. To minimize the overhead, we derive the appropriate QoS levels for several key lengths via an offine polynomial time algorithm. When the risk level is raised online, a real-time task can use a longer key and adapt to the corresponding QoS level (derived offine) in O(1) time.