Interference-aware channel assignment and power allocation for device-to-device communication underlaying cellular network

With the recent advances in radio modules capable of carrying out full duplex (FD) communication, the spectrum efficiency promised by the concept of Cognitive radio (CR) can be stretched even further by incorporating full duplex transceivers in CRs. This incorporation brings about certain challenges in the resource allocation. In this work we try to address some of the challenges in joint channel and power allocation. We proposed a decentralized scheme which is capable of selecting which channels to be used as half duplex and which to be used as FD between a pair of CRs. Then we decomposed the problem into two sub-problems one dealing with channel assignment and the other dealing with power allocation. In the power allocation we did not assume the signal to interference plus noise ratio to be high like most work in the literature. The monotonic optimization framework was used to solve the power allocation problem. Then the channel assignment problem was approximated by a linear objective function and was solved as a linear integer program.

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

In this paper, we aim to maximize the minimum data volume for the energy harvest (EH)-based D2D communication underlaying cellular network. The formulated problem is a mixed integer nonlinear programming (MINLP), which we split into two sub-problems to solve separately. We first employ the Successive Convex Approximation (SCA) method to solve the time and power allocation, while putting the spectrum multiplexed pairs as fixed. Then, we develop a fair allocation mechanism to allocate spectrum resources. Numerical results show that our developed algorithm outperforms other benchmark schemes in terms of the minimum and overall data volumes.

In the paper, the distributed joint spectrum sharing and power allocation scheme is proposed to improve the local services in the Device-to-Device (D2D) communication underlaying cellular networks (CNs) with limited signaling overhead. The scheme aims to optimize the throughput of D2D communications over the shared uplink frequency spectrum meanwhile the base station (BS) protects the cellular users (CUs) from the interference of D2D users. Stackelberg game (SG) is formulated to model the problem for deriving the distributed scheme. In the game, the BS, as a leader, coordinates the interference from the D2D users to the cellular uplinks and maximizes its own profit by pricing the interference. On the other hand, D2D pairs, as followers, compete for the spectrum as a Nash non-cooperative game to maximize their own data rates individually. The pricing mechanism at the BS converging to the Stackelberg Equilibrium is studied. Finally, numerical examples are presented to verify the proposed scheme. It is shown that the scheme is effective on the resource allocation and the CU protection with limited signaling overhead.

Recently, device-to-device (D2D) communication and full duplex communication are both considered key technologies for 5G networks. In this paper, two novel cooperative modes are developed for full duplex D2D communication underlaying cellular networks: the network MU-MIMO based mode (N-mode) and the sequential forwarding mode (S-mode). In the N-mode, two D2D users work as network MIMO to forward the data to cellular users and thus leverage the channel diversity. In the S-mode, the spatial distribution of D2D users and cellular users is explored to improve the transmission rate. Based on these modes, two D2D users share the downlink resources with two nearby cellular users simultaneously to achieve both proximity gain and reuse gain. Moreover, these modes are well suited for edge cellular users. To optimize the performance of the two modes, optimal power allocation is conducted by considering the influence of residual self-interference at full duplex radios and the requirements of the minimum transmission rate for both cellular users and D2D users. Simulation results show that, compared with the dedicated mode, the sum rate of cooperative modes in the perfect self-interference cancellation case can achieve 32% overall improvement and about 70% improvement in the scenario with non-edge D2D users and edge cellular users. With residual self-interference at a full duplex radio, the overall performance improvement is reduced to 15%. However, the cooperative modes can still achieve 40% improvement in the scenario with non-edge D2D users and edge cellular users.

In multi-channel wireless mesh networks (WMNs), it is significantly important to achieve efficient channel utilization. The channel assignment problem, which investigates how to seek a proper mapping between available channels and network interface cards (NICs) on mesh routers (MRs), is attracting more and more attention from the research community. However, most proposed channel assignment approaches assume that NICs are evenly placed on the MRs, and its amount is also pre-determined. Due to the non-uniform distribution of WMN traffic, the equal interface placement will inevitably cause that the bottleneck MRs suffer from NIC shortage, and MRs with less load only have a low utilization of NICs. Hence, in order to improve network performance and reduce network cost, interface placement should be considered carefully in the process of planning a WMN. In this paper, we investigate the joint interface placement and channel assignment problem, i.e., how to appropriately place NICs on MRs and assign channels to them. We first formulate the problem as a mixed integer linear programming (MILP) model, which aims to minimize the total number of NICs while satisfying logical link, link bandwidth, link interference and traffic demand constraints. Then, we propose an MILP-based heuristic algorithm, using iterated local search to obtain sub-optimal solutions efficiently. Finally, we conduct simulation experiments to compare the heuristic solutions with the optimal solutions, and the results show that our proposed heuristic algorithm can achieve good sub-optimal solutions. Moreover, the simulation results also demonstrate that the algorithm can be well applied in large-scale WMNs where the optimal solutions cannot be obtained, and can perform well under different traffic demands.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

Software-defined networking (SDN) along with transmission power control (TPC) have a great potential in enabling efficient wireless networking. Power control aims at increasing network throughput, while SDN provides cognition and intelligent capabilities to network devices. The key challenge in enabling efficient operation of such networks is how to perform efficient power-controlled MAC protocols that includes channel assignment and power allocation such that network throughput is enhanced while using the least number of channels. Traditional MAC protocols for SDNs employ an exclusive channel-occupancy between neighboring secondary users (SUs), which significantly limits network performance. In this paper, we develop a novel power-controlled spectrum access protocol for SDNs based on the interference-channel occupancy model with the objective of increasing network throughput. It allows several concurrent interference-limited transmissions to simultaneously proceed over the same channel in the same neighborhood. Unlike most of previous power-control MAC protocols that perform the channel assignment and power allocation sequentially, our protocol simultaneously makes distributed channel and power assignment decisions for multiple SU transmissions (batch-based method). Batching can be achieved by using an admission control window for SUs to exchange their collision-avoidance control information. Simulation results reveal that compared with CSMA/CA variants, our protocol greatly improve spectrum efficiency, which improves network throughput while reducing energy consumption.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research community because of its capacity to enhance spectral efficiency. NOMA allows multiple users to share the same frequency band and time slot by assigning different power levels and modulation schemes to different users. Furthermore, channel assignment is a critical challenge in OFDMA-NOMA systems that must be addressed to achieve optimal performance. In this context, we propose a solution for both channel and power assignment based on channel condition by splitting the problem into two parts: first, we introduce a novel algorithm to solve the channel user allocation problem, which we refer to as Channel User Sorting and Filling (CUSF). Then, we solve the power allocation problem in two steps: we apply the water filling algorithm at the power assignment and then we implement the Fractional Transmit Power Control (FTPC) algorithm in the NOMA power assignment.

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

This paper proposes a battery and data rate aware resource allocation (BARA) algorithm for quality of experience (QoE) provisioning in D2D communication underlaying cellular networks. The BARA algorithm takes into account the data rate and battery consumption of a user device in spectrum resource allocation, and aims to improve the average QoE of D2D pairs in the system. To this end, the Peukert's law is introduced as a battery model to predict the battery consumption of a user's device. A new QoE model is defined based on the typical MOS model, taking into account a logistic function, to measure the QoE of a user in the system. Moreover, a satisfaction degree function is introduced to decide each D2D user's priority in resource allocation. Simulation results demonstrate that the BARA algorithm can significantly improve the average QoE of D2D pairs in the network without obviously affecting the overall throughput of the system.

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

The channel assignment problem is one of the most important issues in cognitive radio networks (CRNs). Under a SINR-driven model, we consider channel assignments in a network using dynamic source routing (DSR). In this unicasting model, channel assignments are conducted in a relatively small scale of nodes, which are on the chosen route. In addition, we can make use of the route reply (RREP) message in DSR to estimate the SINR and the maximum data transmission rate of nodes on the chosen route. In this way, the source node can conduct the channel assignment in a more efficient way. We propose two algorithms for the single route and multi-route channel assignments, where the multi-route scheme uses alternative nodes to help transmitting. We give a complexity analysis of two algorithms and an extension of reducing complexity for the multi-route channel assignment algorithm. Finally, we conduct simulations of our two algorithms under networks with different densities and show that the performance of our algorithms is efficient.

Multiuser video transmission, where the server transmits videos to multiple users that request different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate the system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and simulation results show that the proposed method can achieve the best system performance.