Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
To meet the explosively increased data traffic demand from 5G and beyond, one of the most promising technologies is densified heterogeneous networks (HetNets). In HetNets, base stations (BSs) are brought closer and closer to users by densifying small BSs, which results in considerable high spectral efficiency and energy efficiency for cellular systems. However, HetNet topology challenges the traditional cellular systems. One of the most important challenges is user association. Aiming to avoid the drawback brought by coupled access (CA) in user association of HetNets, a decoupled access (DA) approach has emerged recently. By allowing access points in uplink (UL) and downlink (DL) association to be different, DA significantly improves UL performance of HetNets. This article focuses on recent works in DA. First, typical scenarios of HetNets for DA are described, which is followed by presenting two major decoupled association policies under typical scenarios. Then, DA shows its superiority over CA under different scenarios and decoupled association policies via various performance metrics. Finally, future work on DA in HetNets is discussed.
When designing an energy efficient massive multiple-input multiple-output (MIMO) system where each receiver antenna is equipped with a low-resolution analog-to-digital converter (ADC), the number of base station(BS)antennas and quantization bits are generally two mutually conflicting system parameters. In this paper, we investigate the joint optimization of the number of BS antennas and ADC resolution in quantized massive MIMO systems, assuming imperfect channel state information(CSI). A tractable approximate expression for the uplink sum spectral efficiency (SE) using maximal ratio combining(MRC)receivers is derived, based on which the pilot length which maximizes the sum SE is put forward. Considering the effect of ADCs, a realistic model of total power consumption is given subsequently. Capitalizing on it, we formulate the optimization problem of selecting the number of BS antennas and ADC resolution to maximize the sum SE under a total power consumption constraint. Our results show that more pilot symbols should be assigned for massive MIMO systems with low-resolution ADCs, especially for the receivers with one-bit quantizers. Moreover, the results show the trade-off between the number of BS antennas and quantization bits. Numerical results suggest that there exists an optimal ADC resolution in massive MIMO systems, while lower quantization bits may cause a substantial degradation of the SE performance and higher one will consume more power.
uniform pricing schemes are proposed, and distributed interference pricing algorithm is proposed to address uniform interference price problem. Simulation results demonstrate that the proposed schemes are effective on interference management and power allocation.
Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access (DSA). However, in a CRN, it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior. In this context, the spectrum occupancy prediction proves to be very useful in enhancing the quality of experience of the secondary user. This paper investigates the practical prowess of various time-series modeling approaches and the machine learning (ML) techniques for predicting spectrum occupancy, based on a spectrum measurement campaign conducted in Jaipur, Rajasthan, India. Moreover, the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data. Nevertheless, prediction through ML-based recurrent neural network proves to perform reasonably well, thereby providing an accurate future spectrum occupancy information for DSA.
The mobile Ad Hoc network (MANET) is a self-organizing and self-configuring wireless network, consisting of a set of mobile nodes. The design of efficient routing protocols for MANET has always been an active area of research. In existing routing algorithms, however, the current work does not scale well enough to ensure route stability when the mobility and distribution of nodes vary with time. In addition, each node in MANET has only limited initial energy, so energy conservation and balance must be taken into account. An efficient routing algorithm should not only be stable but also energy saving and balanced, within the dynamic network environment. To address the above problems, we propose a stable and energy-efficient routing algorithm, based on learning automata (LA) theory for MANET. First, we construct a new node stability measurement model and define an effective energy ratio function. On that basis, we give the node a weighted value, which is used as the iteration parameter for LA. Next, we construct an LA theory-based feedback mechanism for the MANET environment to optimize the selection of available routes and to prove the convergence of our algorithm. The experiments show that our proposed LA-based routing algorithm for MANET achieved the best performance in route survival time, energy consumption, energy balance, and acceptable performance in end-to-end delay and packet delivery ratio.
Energy efficiency is an important metric for downlink transmission in an amplify-and-forward relayaided massive multiple-input multiple-output system, but has not been well investigated. In this work, considering the characteristics of such a system and quality-of-service requirements of users, the energy-efficient joint user association and power allocation problem is studied. First, the closed-form expression of system energy efficiency under the proportional fairness criterion is derived. Then, the proportionally fair utility of system energy efficiency is maximized under constraints of minimum signal-to-noise ratio requirements of users and maximum transmit powers of the base station (BS) and relay stations. As it is difficult to solve this optimization problem directly due to its mixed-integer and non-convex features, the original problem is decomposed into a user association sub-problem and a power allocation sub-problem. For the former, optimum user association is determined by solving a Lagrangian dual problem with a sub-gradient algorithm; for the latter, optimum transmit powers of the BS and each relay station are determined by using Newton’s method. Finally, a sub-optimal solution of the original problem is obtained by a low-complexity iterative algorithm. Simulation results show that the proposed joint user association and power allocation algorithm can offload the traffic of the BS effectively, keep the BS and relay stations operate at low power levels, and improve the system energy efficiency significantly, compared with user association-only schemes.
Femtocell technology is an emerging solution that is promising for improving indoor coverage problems and enhancing cell capacity. In a femtocell network, the overall system performance depends on the access method utilized, which specifies whether a specific user equipment can connect to the femtocell network. Three access methods are defined for long-term evolution (LTE) femtocell networks: open access, closed access and hybrid access. For fair and effective use of resources, hybrid access is preferred. Because some of the resources are shared among nonregistered users, it is essential to regulate their scheduling schemes. This study investigates resource scheduling for femtocell networks in hybrid access mode, which gives preferential access to the high-priority traffic of nonsubscribers. High-priority traffic metric (THP) is calculated for high-priority users, and low-priority traffic metric (TLP) is calculated for low-priority users. Then, individual sorted lists are formed for (THP) and (TLP). Nonsubscribers from the (THP) list are allocated initially, after which UEs from the (TLP) list are chosen for allocation based on resource availability, thereby increasing the overall average throughput of high-priority users in a network.
This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio(CR)networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network.
This paper considers the design of a low-complexity and high-performance precoder for multiple-input multiple-output (MIMO) systems. The precoder is designed by combining both nonlinear and non-iterative processing strategies. The proposed nonlinear precoding techniques employ a nonlinear constellation precoding technique based on maximum distance separable codes at the transmitter. We propose to reduce the computational complexity in iterative-based precoding algorithms by using less complex non-iterative singular value decomposition-based joint precoder and decoder pair design. The maximum likelihood detection for the linear MIMO channel is considered. The simulation results showed that the proposed nonlinear and non-iterative precoding schemes outperform the conventional linear MIMO precoder design, even when a reduced-complexity suboptimal strategy is adopted, considering the bit error rate performance.
