Presence Of Time-varying Channels Research Articles

On the tensor-based approach to adaptive joint channel estimation/data detection in flexible multicarrier MIMO systems

Multiple-input multiple-output (MIMO) systems employing multicarrier modulation (MCM), including flexible MCM that unifies several MCM schemes, have been well studied recently also in their tensor-based formulation. The latter naturally allows for (semi-)blind joint channel estimation/data detection (JCD), of particular interest in future generation massive MIMO systems, given the uniqueness properties of the tensor decomposition (TD) models. Of course, in the presence of time-varying channels, adaptive JCD needs to be implemented. However, the rich literature on recursive TD has only very little been considered in this context and only for orthogonal frequency division multiplexing (OFDM). In this paper, online TD is adapted to realize adaptive JCD for time-varying flexible MIMO-MCM systems. The common assumptions underlying online TD schemes are revisited on this occasion and shown to be inadequate to cope with fast-fading channels. This is demonstrated via simulation results on two of the most popular MCM systems. The results of this work can serve to motivate further study of online TD to extend its applicability in this and other demanding application contexts.

Distributed Channel Selection in Time-Varying Radio Environment: Interference Mitigation Game With Uncoupled Stochastic Learning

This paper investigates the problem of distributed channel selection for interference mitigation in a time-varying radio environment without information exchange. Most existing algorithms, which were originally designed for static channels, are costly and inefficient in the presence of time-varying channels. First, we formulate this problem as a noncooperative game, in which the utility of each player is defined as a function of its experienced expected weighted interference. This game is proven to be an exact potential game with the considered network utility (the expected weighted aggregate interference) serving as the potential function. However, most game-theoretic algorithms are not suitable for the considered network, since they are coupled, i.e., the updating procedure is relying on the actions or payoffs of other players. Then, we propose a simple, completely distributed, and uncoupled stochastic learning algorithm, with which the users learn the desirable channel selections from their individual trial-payoff history. It is analytically shown that the proposed algorithm converges to pure strategy Nash equilibrium in time-varying radio environment; moreover, it achieves optimal channel selection profiles and makes the network interference-free for underloaded or equally loaded scenarios, while achieving, on average, near-optimal performance for overloaded scenarios.

QoS and Channel-Aware Packet Bundling for Capacity Improvement in Cellular Networks

We study the problem of multiple-packet bundling to improve spectral efficiency in cellular networks. The packet size of real-time data, such as voice over Internet Protocol (VoIP), is often very small. However, the common use of time-division multiplexing (TDM) limits the number of VoIP users supported because a packet has to wait until it receives a time slot, and if only one small VoIP packet is placed in a time slot, capacity is wasted. Packet bundling can alleviate such a problem by sharing a time slot among multiple users. A recent revision of cdma2000 1xEV-DO has introduced the concept of the multiuser packet (MUP) in the downlink to overcome limitations on the number of time slots. However, the efficacy of packet bundling is not well understood, particularly in the presence of time-varying channels. We propose a novel quality-of-service (QoS) and channel-aware packet bundling algorithm that takes advantage of adaptive modulation and coding. We show that optimal algorithms are nondeterministic polynomial time (NP)-complete, recommend heuristic approaches, and use analytical performance modeling to show the gains in capacity that can be achieved from our packet bundling algorithms. We show that channel utilization can be significantly increased by slightly delaying some real-time packets within their QoS requirements while bundling those packets with like channel conditions. We validate this paper through extensive OPNET simulations with a complete evolution-data optimized (EV-DO) implementation.