Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130 in large-scale deployments.
Some policies based on spatial time reuse, partition the UEs based on the coloring of the interference graph h @cite_30 @cite_29 @cite_0 @cite_40 @cite_41 , which is not efficient. In general, a set of UEs with the same color (i.e. the UEs who can transmit simultaneously) may not be maximal (See Fig. 1 a), in the sense that there may be UEs who do not interfere but have different colors (we will also show this in the motivating example in Subsection ). In this case, it is more efficient to also let those non-interfering UEs to transmit simultaneously, although they have different colors. Hence, the partitioning based on coloring the interference graph is not efficient, because the average number of active UEs (i.e. the average cardinality of the subsets of UEs with the same color) is low.
@cite_30 @cite_41 @cite_29 @cite_0 @cite_40
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We address interference avoidance by resource partitioning in densely deployed femtocell networks. The main objective is to protect user equipments (UEs) that are located near the cell boundary of two or more femtocells from detrimental downlink interference. The available frequency bands are divided into subbands that are distributed among femtocells in a way that directly adjacent cells do not occupy the same subbands. For this purpose, a novel centrally controlled resource partitioning method is developed based on graph coloring that assigns subbands in terms of resource efficiency. The proposed algorithm strikes a balance between interference protection and spatial frequency reuse of subbands, and is well suited for randomly deployed wireless networks. System-level simulations reveal that cell edge capacities are significantly boosted without causing a degradation in average system throughput. Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold  o . Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of  o . We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity , maximum transmit power of UEs P u , and  o . That is, when P u is a tight operational constraint with respect to (w.r.t.)  and  o , the uplink outage probability and spectral efficiency highly depend on the values of  and  o . In this case, there exists an optimal cutoff threshold  o *, which depends on the system parameters, that minimizes the outage probability. On the other hand, when P u is not a binding operational constraint w.r.t.  and  o , the uplink outage probability and spectral efficiency become independent of  and  o . We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters. The massive multiple-input multiple-output (MIMO) technology has great potential to manage the rapid growth of wireless data traffic. Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing many tens of user equipments (UEs). These gains are only achieved in practice if many more UEs can connect efficiently to the network than today. As the number of UEs increases, while each UE intermittently accesses the network, the random access functionality becomes essential to share the limited number of pilots among the UEs. In this paper, we revisit the random access problem in the Massive MIMO context and develop a reengineered protocol, termed strongest-user collision resolution (SUCRe). An accessing UE asks for a dedicated pilot by sending an uncoordinated random access pilot, with a risk that other UEs send the same pilot. The favorable propagation of massive MIMO channels is utilized to enable distributed collision detection at each UE, thereby determining the strength of the contenders signals and deciding to repeat the pilot if the UE judges that its signal at the receiver is the strongest. The SUCRe protocol resolves the vast majority of all pilot collisions in crowded urban scenarios and continues to admit UEs efficiently in overloaded networks. In this paper, we consider resource allocation optimization problem in cellular networks for different types of users running multiple applications simultaneously. In our proposed model, each user application is assigned a utility function that represents the application type running on the user equipment (UE). The network operators assign a subscription weight to each UE based on its subscription. Each UE assigns an application weight to each of its applications based on the instantaneous usage percentage of the application. Additionally, UEs with higher priority assign applications target rates to their applications. Our objective is to allocate the resources optimally among the UEs and their applications from a single evolved node B (eNodeB) based on a utility proportional fairness policy with priority to realtime application users. A minimum quality of service (QoS) is guaranteed to each UE application based on the UE subscription weight, the UE application weight and the UE application target rate. We propose a two-stage rate allocation algorithm to allocate the eNodeB resources among users and their applications. Finally, we present simulation results for the performance of our rate allocation algorithm.