Reconfigurable and traffic-aware MAC design for virtualized wireless networks via reinforcement learning

In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit (TMAB) and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound

A NOMA-enhanced reconfigurable access scheme with device pairing for M2M networks

This paper aims to address the distinct requirements of machine-to-machine networks, particularly heterogeneity and massive transmissions. To this end, a reconfigurable medium access control (MAC) with the ability to choose a proper access scheme with the optimal configuration for devices based on the network status is proposed. In this scheme, in each frame, a separate time duration is allocated for each of the nonorthogonal multiple access (NOMA)-based, orthogonal multiple access (OMA)-based, and random access-based segments, where the length of each segment can be optimized. To solve this optimization problem, an iterative algorithm consisting of two sub-problems is proposed. The first sub-problem deals with selecting devices for the NOMA/OMA-based transmissions, while the second one optimizes the parameter of the random access scheme. To show the efficacy of the proposed scheme, the results are compared with the reconfigurable scheme which does not support NOMA. The results demonstrate that by using a proper device pairing scheme for the NOMA-based transmissions, the proposed reconfigurable scheme achieves better performance when NOMA is adopted

Efficient LTE/WiFi coexistence in unlicensed spectrum using virtual network entity: Optimization and performance analysis

Long term evolution (LTE) operation in the unlicensed spectrum is a promising solution to address the scarcity of licensed spectrum for cellular networks. Although this approach brings higher capacity for LTE networks, the WiFi performance operating in this band can be significantly degraded. To address this issue, we consider a coordinated structure, in which both networks are controlled by a higher-level network entity. In such a model, LTE users can transmit in the assigned time-slots, while WiFi users can compete with each other by using p-persistent CSMA in their exclusive time-share. In an unsaturated network, at each duty cycle, the TDMA scheduling for LTE users and p values for WiFi users should be efficiently updated by the central controller. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming (CGP) and monomial approximations. Aiming to address Quality-ofService (QoS) assurance for LTE users, an upper bound for average delay of these users are obtained. This analysis could be a basis for admission control of LTE users in unlicensed bands. The simulation results reveal the performance gains of the proposed algorithm in preserving the WiFi throughput requirement

MDP-based MAC design with deterministic backoffs in virtualized 802.11 WLANs

This paper presents MAC protocols for a virtualized 802.11 network aiming to improve network performance and isolation among service providers (SPs). Taking into account the statistical properties of arrival traffic, a Markov Decision Process (MDP) is formulated to maximize the network throughput subject to SP reservations. By introducing the policy tree of the MDP, we present an optimal access policy. Each user can track this policy tree by carrier sensing and learn its transmission opportunity. As computational complexity of the policy tree grows exponentially with the total number of users, an efficient heuristic algorithm is proposed based on the MDP formulation where each user is assigned a deterministic backoff value. Numerical results show that performance of the proposed heuristic algorithm closely matches to the optimal policy. Moreover, both optimal and heuristic algorithms significantly improve TDMA and CSMA in terms of packet delivery ratio and isolation in unsaturated networks

Efficient LTE/WiFi coexistence in unlicensed spectrum using virtual network entity

Due to the increasing demand for mobile traffic, the unlicensed band operation for LTE is proposed by mobile operators. Although by using this approach higher capacity can be achieved for LTE, performance of other wireless technologies operating in this band such as WiFi can be degraded significantly. In order to enable efficient LTE/WiFi coexistence, we consider a coordinated structure via a virtual network entity. LTE users can transmit in the assigned time-slots, while WiFi users can compete with each other by using -persistent CSMA in their exclusive time-share. In an unsaturated network, at each duty cycle, the TDMA scheduling for LTE users and values for WiFi users are updated to maximize the overall network throughput subject to a constraint on the minimum acceptable throughput for WiFi. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming (CGP) and monomial approximations. The simulation results reveal the performance gains of the proposed algorithm in preserving the WiFi throughput requirement

Learning-based hybrid TDMA-CSMA MAC protocol for virtualized 802.11 WLANs

This paper presents an adaptive hybrid TDMA-CSMA MAC protocol to improve network performance and isolation among service providers (SPs) in a virtualized 802.11 network. Aiming to increase network efficiency, wireless virtual-ization provides the means to slice available resources among different SPs, with an urge to keep different slices isolated. Hybrid TDMA-CSMA can be a proper MAC candidate in such scenario benefiting from both the TDMA isolation power and the CSMA opportunistic nature. In this paper, we propose a dynamic MAC that schedules high-traffic users in the TDMA phase with variable size to be determined. Then, the rest of active users compete to access the channel through CSMA. The objective is to search for a scheduling that maximizes the expected sum throughput subject to SP reservations. In the absence of arrival traffic statistics, this scheduling is modeled as a multi-armed bandit (MAB) problem, in which each arm corresponds to a possible scheduling. Due to the dependency between the arms, existing policies are not directly applicable in this problem. Thus, we present an index-based policy where we update and decide based on learning indexes assigned to each user instead of each arm. To update the indexes, in addition to TDMA information, observations from CSMA phase are used, which adds a new exploration phase for the proposed MAB problem. Throughput and isolation performance of the proposed self-exploration-aided index-based policy (SIP) are evaluated by numerical results

Efficient and fair hybrid TDMA-CSMA for virtualized green wireless networks

This paper proposes hybrid TDMA-CSMA for virtualized wireless networks, aiming to meet their isolation requirements. In this scheme, high-load users with non-empty queues are proper and potential candidates for TDMA, while others can compete using p-persistent CSMA. At each superframe, AP decides on TDMA-CSMA scheduling by taking into account traffic parameters of users and slice reservations to maximize the network utilization, while maintaining slice isolation. The corresponding optimization problem is formulated to dynamically schedule users for TDMA phase and optimally pick p parameter for remaining CSMA users. Using complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. The simulation results reveal the performance gains of the proposed algorithm in improving the throughput and keeping isolation in a virtualized wireless network

User association in cloud RANs with massive MIMO

This paper studies a resource allocation problem where a set of users within a specific region is served by cloud radio access network (C-RAN) structure consisting of a set of base-band units (BBUs) connected to a set of radio remote heads (RRHs) equipped with a large number of antennas via limited capacity front-haul links. User association to each RRH, BBU and front-haul link is essential to achieve high rates for cell-edge users under network limitations. We introduce two types of optimization variables to formulate this resource allocation problem: (i) C-RAN user association factor (UAF) including RRH, BBU and front-haul allocation for each user and (ii) power allocation vector. The formulated optimization problem is non-convex with high computational complexity. An efficient two-level iterative approach is proposed. The higher level consists of two steps where, in each step, one of these two optimization variables is fixed to derive the other. At the lower level, by applying different transformations and convexification techniques, the optimization problem in each step is broken down into a sequence of geometric programming (GP) problems to be solved by the successive convex approximation (SCA). Simulation results reveal the effectiveness of the proposed approach to increase the total throughput of network, specifically for cell-edge users. It outperforms the traditional user association approach, in which, each user is first assigned to the RRH with the largest average value of signal strength, and then, based on this fixed user association, front-haul link association and power allocation are optimized

Self-organizing TDMA: a distributed contention-resolution MAC protocol

This paper presents a self-organizing time division multiple access (SO-TDMA) protocol for contention resolution aiming to support delay-sensitive applications. The proposed SOTDMA follows a cognition cycle where each node independently observes the operation environment, learns about the network traffic load, and then makes decisions to adapt the protocol for smart coexistence. Channel access operation in SO-TDMA is similar to carrier-sense multiple-access (CSMA) in the beginning, but then quickly converges to TDMA with an adaptive pseudo-frame structure. This approach has the benefits of TDMA in a highload traffic condition, and overcomes its disadvantages in lowload, heterogeneous traffic scenarios. Furthermore, it supports distributed and asynchronous channel-access operation. These are achieved by adapting the transmission-opportunity duration to the common idle/busy channel state information acquired by each node, without any explicit message passing among nodes. The process of adjusting the transmission duration is modeled as a congestion control problem to develop an additive-increasemultiplicative-decrease (AIMD) algorithm, which monotonically converges to fairness. Furthermore, the initial access phase of SO-TDMA is modeled as a Markov chain with one absorbing state and its required convergence time is studied accordingly. Performance of SO-TDMA in terms of effective capacity, system throughput, collision probability, delay-outage probability and fairness is investigated. Simulation results illustrate its effectiveness in performance improvement, approaching the ideal case that needs complete and precise information about the queue length and the channel conditions of all nodes

Learning-based hybrid TDMA-CSMA MAC protocol for virtualized 802.11 WLANs

This paper presents an adaptive hybrid TDMA-CSMA MAC protocol to improve network performance and isolation among service providers (SPs) in a virtualized 802.11 network. Aiming to increase network efficiency, wireless virtual-ization provides the means to slice available resources among different SPs, with an urge to keep different slices isolated. Hybrid TDMA-CSMA can be a proper MAC candidate in such scenario benefiting from both the TDMA isolation power and the CSMA opportunistic nature. In this paper, we propose a dynamic MAC that schedules high-traffic users in the TDMA phase with variable size to be determined. Then, the rest of active users compete to access the channel through CSMA. The objective is to search for a scheduling that maximizes the expected sum throughput subject to SP reservations. In the absence of arrival traffic statistics, this scheduling is modeled as a multi-armed bandit (MAB) problem, in which each arm corresponds to a possible scheduling. Due to the dependency between the arms, existing policies are not directly applicable in this problem. Thus, we present an index-based policy where we update and decide based on learning indexes assigned to each user instead of each arm. To update the indexes, in addition to TDMA information, observations from CSMA phase are used, which adds a new exploration phase for the proposed MAB problem. Throughput and isolation performance of the proposed self-exploration-aided index-based policy (SIP) are evaluated by numerical results