Search

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants’ priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS), and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association, and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness, and QoS performance compared with several baseline schemes.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. We analyze the achievable rate loss due to channel state information (CSI) mismatch arising from both channel estimation and quantization feedback. We first derive an upper bound to characterize the effect of imperfect CSI, and then present a limited feedback strategy for both the two-hop channels in the relay system. This newly presented feedback strategy reveals the relationship among the CSI quantization levels, the transmit power, and the pilots for channel estimation. An optimized pilot design at the base station and the relay is also presented by applying the derived bounds for the two-hop system.

In this paper, we present an approximation formula and the close-form expression for the sum rate of the transmit and the receive zero-forcing (ZF) multiple-input multiple-output (MIMO) broadcast systems with user selection, respectively. Instead of assuming a large number of users to obtain a scaling law as most current work, we derive the sum rate formulas of the ZF MIMO broadcast systems with a small number of scheduled users. By analysis and simulations, we find that when taking the variations of feedback channel into account, the receive ZF MIMO broadcast system is more robust to feedback errors and can deliver equal or even higher sum rate than the transmit ZF MIMO broadcast system. We discuss whether a feedback channel is suitable to send channel state information (CSI) for calculating transmit antenna beamforming weights, or suitable to send CSI for selecting users in the receive ZF MIMO broadcast system. Our results show that as the variation of feedback channel errors increases from 0.5 to 1.5, the receive ZF 3 × 3 MIMO broadcast system can provide 36% to 116% higher sum rate than the transmit ZF 3 × 3 MIMO broadcast system in the case of 20 users at signal to noise ratio (SNR) equal to 20 dB. Providing that more feedback bandwidth and an error-free feedback channel are available, the transmit ZF MIMO broadcast system can achieve higher sum rate than the receive ZF MIMO broadcast system.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a cooperative multi-group priority (CMGP) based MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can bypass the computationally-intensive active user identification process. Moreover, our method can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. The results allow us to investigate the throughput performance of the proposed CMGP protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

The paper proposes a cellular neural network and utility (CNNU)-based radio resource scheduler for multimedia CDMA communication systems supporting differentiated quality-of-service (QoS). Here, we define a relevant utility function for each connection, which is its radio resource function weighted by a QoS requirement deviation function and a fairness compensation function. We also propose cellular neural networks (CNN) to design the utility-based radio resource scheduler according to the Lyapunov method to solve the constrained optimization problem. The CNN is powerful for complicated optimization problems and has been proved that it can rapidly converge to a desired equilibrium; the utility-based scheduling algorithm can efficiently utilize the radio resource for system, keep the QoS requirements of connections guaranteed, and provide the weighted fairness for connections. Therefore, the CNNU-based scheduler, which determines a radio resource assignment vector for all connections by maximizing an overall system utility, can achieve high system throughput and keep the performance measures of all connections to meet their QoS requirements. Simulation results show that the CNNU-based scheduler attains the average system throughput greater than the EXP and the HOLPRO scheduling schemes by an amount of 23% and 33%, respectively, in the QoS guaranteed region.

Recent studies have revealed that the remarkable capacity improvement resulting from an open-loop multiple-input-multiple-output (MIMO) spatial multiplexing system may come at the sacrifice of degrading link reliability. This tradeoff between antenna multiplexing gain against antenna diversity gain may translate into smaller coverage areas. In this paper, we suggest using the multiuser diversity to replenish the diversity-deficient spatial multiplexing MIMO system. Specifically, we propose a fair scheduling scheme, called the strongest-weakest-normalized-subchannel-first (SWNSF) scheduling, which requires only limited amount of feedback. Our analysis and results indicate that the SWNSF scheduling can significantly increase the coverage of the multiuser MIMO system while further improving the system capacity.

In this paper, we prove that the problem of maximizing data throughput by adaptive modulation and power control while meeting packet error requirements is NP-complete. A heuristic algorithm for integrated link adaptation and power control is, thus, proposed to achieve specified error rates and to improve overall throughput for real-time applications in broadband wireless packet networks. The algorithm divides terminals into groups according to their signal path gains and periodically adapts transmissions based on the required error rates, actual error statistics, and average transmission power of each terminal group. Transmission power is adjusted by an enhanced Kalman-filter method to ensure successful reception. Extensive simulation results reveal that the algorithm consistently delivers the specified error performance and attempts to maximize network throughput for a wide range of parameter settings.