We explore the performance of different resource allocation schemes for transferring elastic traffic in a cellular network that is either overlaid or underlaid with D2D traffic. To this end, we model a single cell during uplink transmissions and jointly consider the presence of a randomly varying number of D2D and cellular users in the system. We use different processor sharing queueing models to characterize the performance of the overlaying and underlaying schemes and measure the performance as the mean flow level delay. In the overlaying approach, depending on the load a certain fraction of the radio resources is reserved for the D2D traffic and the cellular traffic, and hence there is no interference between the D2D and cellular users. In the underlaying approach, the D2D users are allowed to opportunistically transmit unless being interfered by a cellular user nearby. Our numerical studies reveal that the underlaying D2D traffic scheme provides a good performance compared to other methods, especially if the interference range of a cellular user is small compared with the cell dimensions. Moreover, the so-called dynamic overlay method we propose appears to perform better than the static overlay scheme.

We study the cell selection techniques for M2M traffic between an LTE macrocell and WLAN femtocells in a heterogeneous network deployment scenario. With the dense deployment of femtocells (operating in WLAN), M2M traffic can primarily be served by them while the macrocell (operating in LTE) can be used by the machines in case of congestion in their own femtocell. We study various load balancing strategies that aid the machines to select a proper cell in such a multi-RAT heterogeneous network deployment scenario so that the access delay for the M2M traffic is minimized. In particular, we derive the optimal static policy of choosing between the WLAN femtocell and the LTE macrocell. In addition, we develop dynamic policies based on the information about the arrivals and the number of backlogged users, and compare their performance against each other and with the optimal static policy. Our results indicate that the potential gains from the dynamic policies can be significant. Moreover, simple backlog-based heuristics perform close to or better than the optimal static policy.

As machine-to-machine applications using cellular systems become pervasive, it is an important concern that their deployment does not jeopardize the performance of the cellular systems. Support for a massive number of machines brings technical challenges affecting the performance of the random access channel and efficiency of radio resource allocation. Capillary networks are considered as an extensions to the cellular systems for providing large-scale connectivity. This paper proposes an aggregation scheme for capillary networks connected to the LTE network to improve their communication efficiency. A gateway, an intermediate unit between machines and the base station, aggregates packets from the machines during a predefined time, and then delivers them to the LTE network. In addition, this paper analyzes the trade-offs between random access interaction, resource allocation, and communication latency. Results reveals that accepting the extra latency for accumulating packets can significantly reduce the random access requests and the required resources for the data transmissions.

We consider the optimal opportunistic scheduling problem for downlink data traffic in a wireless cell with time-varying channels. The scheduler itself operates in a very fast timescale of milliseconds, but the objective function is related to minimizing the holding costs in a much longer timescale, at the so-called flow level. The Whittle index approach is a powerful tool in this context, since it renders the flow level optimization problem with heterogeneous users tractable. Until now, this approach has been applied to the opportunistic scheduling problem to generate non-anticipating index policies that may depend on the amount of attained service but do not utilize the exact size information. In this paper, we produce a size-aware (i.e., anticipating) index policy by applying the Whittle index approach in a novel way. By a numerical study based on simulations, we demonstrate that the resulting size-aware index policy systematically improves performance. As a side result, we show that the opportunistic scheduling problem is indexable when the file sizes follow the Pascal distribution, and we derive the corresponding Whittle index, which generalizes earlier results.

In heterogeneous LTE networks, an incoming user can either join the femto or the macro base station at the random access stage. We consider a system that has a single macro base station and a number of femtocells in its coverage area. We study the problem of optimally choosing either the femto or the macro station based on the knowledge of the traffic arrival rate and the number of backlogged users in both cells. In this paper, we derive the optimal static policy of choosing the base stations that minimizes the average access delay. We also develop various dynamic policies based on the information about the arrivals and backlogged users, and compare their performance against each other and with the optimal static policy. We observe that some of these dynamic policies give very good performance, which provides a lower bound of performance. In addition, a dynamic policy that utilizes only the backlog levels, although not always as good as the optimal static policy, is robust and still stable for a wide range of arrival rates.

We consider service systems where new jobs not only increase the load but also improve the service ability of such a system, cf. opportunistic scheduling gain in wireless systems. We study the optimal trade-off between the SRPT (Shortest Remaining Processing Time) discipline and opportunistic scheduling in the systems characterized by compact and symmetric capacity regions. The objective is to minimize the mean delay in a transient setting where all jobs are available at time 0 and no new jobs arrive thereafter. Our main result gives conditions under which the optimal rate vector does not depend on the sizes of the jobs as long as their order (in size) remains the same. In addition, it shows that in this case the optimal policy applies the SRPT principle serving the shortest job with the highest rate of the optimal rate vector, the second shortest with the second highest rate etc. We also give a recursive algorithm to determine both the optimal rate vector and the minimum mean delay. In some special cases, the rate vector, as well as the minimum mean delay, have even explicit expressions as demonstrated in the paper. For the general case, we derive both an upper bound and a lower bound of the minimum mean delay.