By allowing multiple users to transmit using the same frequency band at the same time, non-orthogonal mul-tiple access (NOMA) can support more users as compared to orthogonal multiple access (OMA) given a fixed amount of time-frequency resources. In this paper, we study the resource allocation problem in the uplink of a single-cell network when NOMA is enabled, where the maximum completion time of serving all connected users is to be minimized. While such an objective function minimizes the required time to serve uplink users, the resource allocation problem is NP-hard. We propose a serial collaborative optimization framework based on simulated annealing (SA) to search for the optimal user pairing and scheduling solution. Simulation studies show that the proposed algorithm for NOMA scheduling can reduce the maximum completion time by more than 30% when compared against OMA scheduling and random NOMA user clustering.

The user pairing problem for cache-based timeslotted non-orthogonal multiple access (NOMA) system with index coding is investigated. During each time slot, the packets of two users are scheduled at the base station. In accordance with different cache information of the scheduled users, either superposition coding or index coding is applied for base station transmission. For some specific case, the superior performance on the aspect of power consumption of index coding compared to that of superposition coding is analyzed. Besides, the power saving of our design system when compared to that of the NOMA system with pure superposition coding is also demonstrated in a mathematical way. Subsequently, we show that the original user scheduling problem can be transformed in quadratic time into a minimum weight perfect matching problem of an undirected graph, which can be solved with time complexity O(K3), where K is the number of users. Based on this transformation, the feasibility of any given system is analyzed. Furthermore, we formulate the minimum weight perfect matching problem as an integer linear problem and solve it by integer linear programming. Numerical results validate the performance gains of our proposed system from the aspects of total transmit power and outage probability.

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO's problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

With dual connectivity, a mobile user can be served by a macro base station (MBS) and a pico base station (PBS) simultaneously. In this paper, we address the problem of optimizing user-PBS association and power allocation in the uplink such that the network can serve the users' demand at the minimum cost, where the PBSs are subject to backhaul capacity limitations and minimum rate requirements of users. We show that this non-convex problem can be formulated as a signomial geometric programming (SGP) whose solution can be found by solving a series of geometric programming (GP) problems. Simulation results are provided to demonstrate traffic offloading trend to PBSs for different cost and backhaul capacity settings, confirming the effectiveness of the proposed iterative algorithm. They also show that the output of the proposed algorithm closely matches the global optimal solution with affordable complexity.

To obtain good network performance in Long Term Evolution-Advanced (LTE-A) heterogeneous networks (HetNets), enhanced inter-cell interference coordination (eICIC) and further eICIC (FeICIC) have been proposed by LTE standardization bodies to address the entangled inter-cell interference and the user association problems. We propose the distributed algorithms based on the exact potential game framework for both eICIC and FeICIC optimizations. We demonstrate via simulations a 64% gain on energy efficiency (EE) achieved by eICIC and another 17% gain on EE achieved by FeICIC. We also show that FeICIC can bring other significant gains in terms of cell-edge throughput, spectral efficiency, and fairness among user throughputs. Moreover, we propose a downlink scheduler based on a cake-cutting algorithm that can further improve the performance of the optimization algorithms compared with conventional schedulers.

We investigate a pricing based approach to control interference from a tier-2 base station (BS) to a tier-1 BS in reversed time division duplex (TDD) multi-antenna systems. The tier-2 BS is being charged for causing interference to the tier-1 BS. Also, the tier-2 BS has to satisfy the signal-to-interference-plus-noise ratio (SINR) targets of its downlink users under a maximum transmission power constraint. Analytical and simulation studies are carried out to understand the behavior of the tier-2 BS for different charges and for different power budgets. Observations from the analyses suggest that the tier-1 BS can perform interference control and/or profit maximization without knowing the downlink channels of the tier-2 BS.

In this paper we propose a distributed algorithm for jointly optimizing almost blank subframe (ABS) and cell selection bias (CSB) patterns in Long Term Evolution- Advanced (LTE-A) heterogeneous networks (HetNets). We formulate the optimization problem as an exact potential game, where a Nash equilibrium point is guaranteed to be achieved within finite number of plays. Through simulations, we are able to demonstrate the fast convergence of the algorithm, an increase in average user rate, and a tremendous improvement on the service fairness of the users.

To optimize the network utility in 3GPP Long Term Evolution-Advanced (LTE-A) heterogeneous networks (HetNets), it is necessary to jointly consider inter-cell interference mitigation and user attachment. Based on potential game formulation, we optimize almost blank subframe (ABS) and/or cell selection bias (CSB) settings for both macrocells and picocells in a distributed manner. We demonstrate the need of joint ABS and CSB optimization via simulation case studies. Extensive simulations confirm that joint ABS and CSB optimizations can lead to a 20% improvement in spectral efficiency and a 46% improvement in energy efficiency while increasing the fairness of the achieved rates of users.

In erasure broadcast channels, network coding has been demonstrated to be an efficient way to satisfy each user's demand. However, the erasure broadcast channel model does not fully characterize the information available in a "lost" packet, and therefore any retransmission schemes designed based on the erasure broadcast channel model cannot make use of that information. In this paper, we characterize the quality of erroneous packets by Signal-to-Noise Ratio (SNR) and then design a network coding retransmission scheme with the knowledge of the SNRs of the erroneous packets, so that a user can immediately decode two source packets upon reception of a useful retransmission packet. We demonstrate that our proposed scheme, namely Quality-Aware Instantly Decodable Network Coding (QAIDNC), can increase the transmission efficiency significantly compared to the existing Instantly Decodable Network Coding (IDNC) and Random Linear Network Coding (RLNC).