This paper presents a learning-based multiple-access scheme for machine-type communication networks where the base station (BS) is equipped with a large-scale antenna array and devices have spatially correlated and event-based traffic. Each access time frame is divided into two segments: grant-based and grant-free, which are dynamically configured according to device traffic statistics to achieve high throughput. As device traffic statistical characteristics might not be available at the BS in reality, we propose a learning-based algorithm that exploits device traffic correlations to predict the probability of having data packets of each device in the next time frame based on its traffic history. The BS uses this predicted probability for its scheduling and access probability calculation algorithms to maximize the system throughput. Performance of the proposed learning-based multiple-scheme is evaluated and illustrative results show its superior achievable throughput as compared to both random scheduling and optimized random access schemes.

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.

In this paper, we propose a multiple access scheme for machine-type communications for which high spectral efficiency and massive connectivity are demanded. To meet these requirements, we enhance the proposed access scheme with the reconfigurability feature to properly divide each time frame to three segments of grant-based NOMA, grant-based OMA, and random access. To obtain the length of each segment, an optimization problem is formulated which is solved by dividing it into two sub-problems. The first sub-problem nominates devices for the NOMA transmissions, while the second sub-problem derives the length of each segment as well as the the parameter of the random access-based scheme.

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 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 and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound.

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 Wi-Fi 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 Wi-Fi users can compete with each other by using p-persistent carrier sense multiple access (CSMA) in their exclusive timeshare. In an unsaturated network, at each duty cycle, the timedivision multiple access (TDMA) scheduling for LTE users and p values for Wi-Fi 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 and monomial approximations. Aiming to address the qualityof-service assurance for LTE users, an upper bound for average delay of these users is obtained. This analysis could be a basis for the admission control of LTE users in unlicensed bands. The simulation results reveal the performance gains of the proposed algorithm in preserving the Wi-Fi throughput requirement.

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 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 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.

In this paper, we investigate the dynamic user-AP association with QoS-aware CSMA in a virtualized wireless network. In particular, to efficiently support slice isolation, we consider an enhanced CSMA frame with two phases: a contention-free A-phase using CSMA with deterministic back-off, followed by a contention-based C-phase using p-persistent CSMA. The user scheduling in the contention-free A-phase and determination of p values for the contention-based C-phase along with the user-AP association are formulated as an optimization problem, aiming to maximize the overall network throughput under the slice isolation constraints and channel conditions. Subsequently, complementary geometric programming (CGP) and monomial approximations are used to develop an iterative algorithm for solution. Illustrative simulation results show the efficiency of the developed solution and its effect on network performance while maintaining the isolation among slices.

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.

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.