The exponential growth of data traffic volume dominates the demand for the next generation mobile networks (5G). The consistent and satisfied quality of experience (QoE) is one of the leading challenges of provisioning services in 5G mobile networks. Thus, in this paper, we propose a novel adaptive prefetching scheme to compensate the undesired transmission conditions in the 5G mobile network by extending the content prefetching concept from the users to the network. Specifically, an optimization problem is proposed for a prefetching scheme that adaptively retrieves users' data to access nodes and (or) user equipments (UEs) before the actual requests according to the network status, QoE status, predicted data rates, and mobility patterns of users. For the sake of tractability, the prefetching problem is transferred to a convex problem that can be solved efficiently. Accordingly, to implement the proposed schemes in the 5G network, system interactions among entities in the network are designed to realize prefetching-related functions. A signaling protocol to support the adaptive prefetching scheme is also presented. Simulation results show that an adaptive prefetching scheme can improve the network performance significantly.

In this paper, we present low-complexity algorithms for cache placement problem in software-defined radio access networks. Our key design target is that the packet delay from any cache to its associated users is below a threshold. This feature is a significant improvement compared to conventional approaches relying on the average hop count. We introduce a traffic engineering solution for joint multiserver selection and rate allocation. This algorithm is used to minimize the cache placement cost function, such as delivery cost per bit. Our cache placement method allows prediction of user throughput and thus the network throughput. Extensive simulation results verify that our method can give optimal or near optimal network throughput while guaranteeing packet delay performance.

We consider traffic engineering (TE) in networks where quality of service (QoS)-guaranteed as well as best effort (BE) services are present. We propose a joint TE optimization technique for both QoS and BE traffic using multi-objective optimization framework. We show that joint TE optimization results in more efficient network resource usage compared to sequential TE methodology where TE decision (finding paths and traffic splitting among paths) is made for QoS-guaranteed traffic first by assigning appropriate network resources and then TE decision is made for BE traffic according to remaining network resources. Furthermore, we consider QoS traffic demand fluctuations and propose a joint TE problem formulation to address both QoS traffic demand fluctuations and efficient network resource usage for BE traffic.

We explore the effect of imperfect knowledge on multipoint downlink traffic engineering (TE) (with dynamic point selection DPS) computed centrally and jointly with power control (PC) optimization versus distributed PC techniques. Instead of considering a single metric that shows qualities in a single dimension, we observe both performance and resilience by observing how many users get quality of experience (QoE) — or otherwise, how many users would complain that they do not receive the expected service. We identify that centralized traffic engineering provides the best performance (highest class of service) with distributed power control in a practical (imperfect) network. Although this has a clear drawback as it comes with a low resilience in face of bad service admission. We conclude that fifth generation networks will need to be able to dynamically change optimizing strategies to face different loading of the networks.

We investigate the cooperative transmission design for the cloud radio access network (C-RAN) considering fronthaul capacity and cloud processing constraints. Specifically, we consider the joint transmission scheme where the baseband signals and precoding vectors are processed and calculated by the cloud, which are delivered over the fronthaul links to the remote radio heads (RRHs) to form the RF signals for being transmitted to the users. We formulate the joint optimization problem for precoding design and allocation of RRHs, fronthaul capacity, and BBU processing resources to minimize the total transmission power subject to QoS constraints of the users. We present both optimal exhaustive search algorithm and two low-complexity algorithms to solve the resource allocation problem where the first one can achieve the Pareto optimality and the second one can determine an efficient solution with pretty low complexity. Numerical results confirm the excellent performance of the proposed low-complexity algorithms.

A generalized method, namely non-uniform perturbation, to disign perturbation signal and decoder for the multiuser vector perturbation (VP) precoding is proposed. In this method, the perturbation signal at the transmitter can take integer and non-integer values. A new decoding method is presented for this new signal design, in which the statistic of perturbation signal is not required to remove the perturbation signal at the receiver. Compared with the conventional VP method, the non-uniform perturbation scheme yields significant performance gain.

This paper presents an optimization framework for downlink transmission parameters of mobile cellular systems. A typical network optimization approach is to divide the network into disjoint clusters of base-stations (BS). Optimization is then performed within each cluster for important parameters such as transmit power, precoder, etc. This approach is widely adopted in academic research and industrial standard bodies, e.g. 3GPP LTE-Advanced. While reducing the optimization complexity, this strategy suffers from a performance limit due to interference from the nodes outside cluster. We thus propose a framework to overcome this limit by allowing clusters to exchange their parameters and optimization information via low-rate and non-zero delay backhauls. System-level simulations for LTE downlink transmit power optimization show that the proposed optimization model, while having the low-complexity of cluster-based approach, could nearly achieve the performance of network-wise optimization. Therefore, this model is particularly suitable for optimization of 4G and beyond-4G cellular networks.

Co-channel interference is one of the major factors limiting the capacity in modern cellular networks. Interference rejection combining (IRC) is an effective technique to suppress co-channel interference, requiring receivers with multiple antennas. For terminals using IRC, the larger the number of available antennas, the more interfering signals that can be suppressed. However, production cost, size and energy consumption of the required radio-frequency units impose a limit in the number of practical antenna elements. In this paper, we investigate the combination of IRC with antenna selection (AS), as a cost-effective approach to improve the interference suppression performance of wireless receivers, and present different low-complexity AS algorithms. In addition, we propose algorithmic modifications in case the estimates of interfering channels are unknown at the receiver, and an estimate of the interference-plus-noise covariance matrix (CM) has to be used. It is found that, in the presence of channel estimation errors, AS techniques using the CM approach are able to provide better performance than techniques using estimates of interfering channels. These findings and the effectiveness of the proposed algorithms are validated by both computer simulations and experimental testing using a hardware prototype.

Methods to design combining vectors at the receiver for multiantenna users in the multiuser multiple-input multiple-output downlink channel are presented. The proposed methods are applicable for linear and nonlinear precoding. A fast user ordering algorithm is provided to reduce computational complexity. Furthermore, an adaptive codebook for combining vectors is presented to decrease the precoding complexity and the amount of overhead information in the downlink. Simulation results show significant gains compared with other methods such as receive antenna selection or dominant eigenmode combining.

A method to design receive beamforming vectors with user selection is proposed for the multiuser Tomlinson-Harashima precoding, where each user is equipped with multiple antennas. The method exploits the relationship between LQ decomposition and Gram-Schmidt process to jointly optimize the receive beamforming vectors of users. Furthermore, a new approach is presented to reduce the amount of information required to send the beamforming vectors from the base station to users. Simulation results are provided to support the advantages of the proposed schemes.

In this paper, a new class of full-diversity, rate-one space–time block codes (STBCs) called semiorthogonal algebraic space–time block codes (SAST codes) is proposed. SAST codes are delay optimal when the number of transmit antennas is even. The SAST codeword matrix has a generalized Alamouti structure where the transmitted symbols are replaced by circulant matrices and the commutativity of circulant matrices simplifies the detection of transmit symbols. SAST codes with maximal coding gain are constructed by using rate-one linear threaded algebraic space–time (LTAST) codes. Compared with LTSAT codes, SAST codes not only reduce the complexity of maximum-likelihood detection, but also provide remarkable performance gain. They also outperform other STBC with rate one or less. SAST codes also perform well with suboptimal detectors such as the vertical-Bell Laboratories layered space–time (V-BLAST) nulling and cancellation receiver. Finally, SAST codes attain nearly 100% of the Shannon capacity of open-loop multiple-input–single-output (MISO) channels.

User selection for multiuser linear precoding in broadcast channel is investigated in this paper. Assuming complete knowledge of channel state information at the transmitter, new user selection algorithms are introduced using minimum mean square error criterion. A novel iterative user selection is proposed offering a flexible performance-complexity tradeoff. New algorithms are also developed for orthogonal frequency division multiple access systems. Simulation results show that the proposed methods outperform well-known algorithms, which select users based on users' orthogonality or sum rate bound.