In this paper, we propose an uplink system which combines power-domain non-orthogonal multiple access (PD-NOMA) and sparse code multiple access (SCMA) together, in order to enable massive user connectivity. The receiver adopts successive interference cancellation (SIC) and iterative SCMA detection algorithm for decoding the superimposed signals. An optimization method of the code rate for the error-correcting channel codes is proposed by analyzing the extrinsic information transfer (EXIT) charts, so as to reach a trade-off between system convergence behavior and spectral efficiency. To evaluate the performance, a conventional SCMA system is used as comparison baseline. Numerical results show that the proposed system along with the optimization method achieves good bit error rate (BER) performance and significantly improves the spectral efficiency.

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.

We investigate the blind identification and separation of underdetermined linear instantaneous mixtures with a single sensor and an arbitrary known number of sources with finite known support and uniform distribution. We propose channel estimators based on the high-order statistics of the received signal and on the rotational symmetries of the source constellations. Explicit expressions for distinct and equal rotation orders are derived. The proposed estimators are used as initializers for the iterative least squares with enumeration algorithm to enhance its convergence properties. Simulation results on several high interference two-user wireless communication scenarios show that our initialization strategy achieves a substantial performance gain compared to other traditional initializers.

This paper studies the joint multiplexing, Resource-Block and power allocation problem in underlay Device-to-Device (D2D) communications. The interfering signals are assumed fully asynchronous at the device receivers and at the Base Station. Inter-Channel Interference (ICI) then generates major impairment on the expected rate gain if Orthogonal Frequency Division Multiplex (OFDM) multi-carrier modulation is used, contrary to new multi-carrier modulations such as Filter Bank Multi-Carrier (FBMC) or Fast Fourier Transform (FFT)-FBMC. The proposed centralized algorithm provides a lower bound to the maximum sum rate over D2D pairs, taking into account ICI. It increases the sum data rate compared to a Frequency Division Multiple Access (FDMA) technique and compared to two other algorithms allowing multiplexing and applying power control.

This paper studies two different multiplexing problems for Device to Device (D2D) asynchronous communications with different multi-carrier modulations. Several D2D pairs may be multiplexed on the same Resource Blocks (RB) if they are jointly able to reach a high Signal to Interference plus Noise Ratio (SINR). In asynchronous communications, inter-channel interference (ICI) is present on top of co-channel interference, and the multiplexing problem can consequently not be split per RB since RB are no longer orthogonal. In this paper, we derive the system model that takes into account ICI and then study the maximum multiplexing problem. Two heuristics based on the Frobenius and the infinity norms that lead to lower bounds on the multiplexing gain are proposed. Then, the maximum sum SINR problem is studied with the infinity norm, showing that higher rates are achieved with Filter Bank Multi-Carrier (FBMC) modulation than with Orthogonal Frequency Division Multiplex (OFDM).

We consider the downlink resource allocation for a multi-carrier-based asynchronous cognitive radio network with statistical knowledge of the channel state information of both primary and secondary systems available at the secondary base station. The problem of secondary utility maximization is investigated under primary and secondary user outage constraints and total power constraint. Analyses are provided for cases where both primary and secondary systems are utilizing either orthogonal frequency-division multiplexing or filter-bank based multi-carrier modulations. The highly complex yet challenging and non-convex joint power-subcarrier allocation problem is tackled by leveraging the principle of alternating optimization techniques. A per-carrier outage-based solution is provided for the subcarrier allocation stage. In the second stage, we introduce an upper bound to the outage constraints to ensure tractability. We propose an efficient convex approximation method to obtain near-optimal solutions of the centralized power control problem. Furthermore, to reduce computational complexity, a distributed implementation based on dual decomposition method is designed. Extensive simulation results are further provided to corroborate the validity of the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

This paper studies resource allocation for a multi-carrier based cognitive radio network under the assumption of statistical channel state information (CSI). To circumvent the inherent high computational complexity investigating the joint power-rate-subcarrier of the outage-constrained sum rate maximization problem, we adopt a sub-optimal strategy by solving independently the subcarrier and power-rate problem. Firstly, we propose a heuristic subcarrier allocation paradigm by utilizing an outage-based metric. Secondly, we conservatively approximate the intractable non-convex powerrate control problem and propose a sequential-based algorithm to efficiently obtain a solution to the problem. The proposed algorithm has been shown to converge to solutions that are stationary points of the original power-rate problem. Extensive simulation results are provided to demonstrate the efficiency of our proposed algorithm.

This paper studies the weighted sum rate maximization problem for device-to-device (D2D) underlay communications. Since each D2D transmitter is synchronized with its receiver, all D2D receivers get asynchronous interference from the other transmitters, and so does the Base Station (BS). This is modelled by a uniformly-distributed timing offset which generates inter-channel interferences in multi-carrier transmissions. Filter Bank based Multi-Carrier (FBMC) is then far more efficient than Orthogonal Frequency Division Multiplex (OFDM) to limit the inter-channel interference spread. In this context, D2D pairs are clustered through graph coloring in order to avoid high interference situations. Then we derive the weighted sum rate maximization algorithm with inter-channel interference and with an interference constraint at the BS. An iterative implementation of this algorithm converges to the global optimal solution, when the high Signal-to-Interference-plus-Noise Ratio (SINR) condition holds. Simulation results show that D2D users can be efficiently multiplexed, achieving high data rates while fulfilling the BS interference constraint.

Resource allocation for an uplink cooperative OFDMA system is considered in this paper. Relays are simple mobile users with advantageous positions to relay other users with bad channel conditions. Having their own data to transmit to the Base Station (BS), the relays multiplex their own data to relayed data before transmitting to BS using Decode and Forward (DF) scheme. The resource allocation is formulated as an optimization problem to minimize the transmit system power while ensuring a target data rate per user. The considered features to optimize are relay selection, power and Resource Blocks (RB) allocation. Dual decomposition is used for resolution. Both half and full-duplex users cases are studied and compared to non-cooperative system. Simulation results show that our proposed algorithm reduces system's power consumption while ensuring required QoS.

In this paper, we study the problem of resource allocation for an opportunistic spectrum-sharing channel under the assumption of statistical channel state information (CSI). More specifically, we assume that statistical CSI of the channels between both primary and secondary transmitters and the primary receiver is available at the secondary transmitter. We investigate the achievable rate maximization of both primary and secondary systems with successive interference cancellation (SIC) under outage, interference limit, and total power constraints. We deal with the secondary non-convex optimization problem by using the first order Taylor approximation. We propose a sequential convex approximation algorithm to solve the power allocation problem. Simulation results establish the efficiency of our proposed algorithm.

This paper addresses the problem of energy-efficiency (EE) within an underlay cognitive radio (CR) network where primary users (PUs) and secondary users (SUs) utilize two types of multicarrier modulation: orthogonal frequency-division multiplexing (OFDM) and filter bank based multicarrier (FBMC). The problem of non-cooperative SU downlink energy-efficiency is investigated using a game theoretic approach. We reformulate the EE optimization problem as a non-cooperative power allocation game (NPAG). A distributed power control algorithm is proposed to find the optimal power allocation strategy for each secondary base station on each subcarrier. The proposed non-cooperative game is demonstrated to reach a Nash-equilibrium (NE) point. Simulation analyses are then provided in order to validate the efficiency of our proposed distributed algorithm and also to highlight the advantages of using FBMC as a modulation technique if compared to OFDM.

We investigate in this paper the resource allocation problem in an uplink cooperative MIMO OFDMA multiuser system. This system may be used in 5G in the context of green communications since we aim to save energy by reducing the system transmit power. We focus on multiplexing Decode and Forward (DF) relays that have their own data to transmit in addition to data to relay. Aiming to minimize the system transmit power with a required target data rate per user, the resource allocation problem is formulated as an optimization problem. The feature to optimize are relay selection, power allocation and resource block (RB) allocation. We propose a novel resolution algorithm based on dual method and Lagrangian decomposition. Moreover, at each user, Equal Power Allocation (EPA) and beamforming are studied and compared to allocate power to antennas. Simulation results show that the cooperative proposed algorithm leads to an important improvement comparing to a non-cooperative system.

This paper discusses the resource allocation problem in an uplink multi-cell OFDMA system. The classical LTE model considers two areas in the cell: the cell center and the cell border and divides the resources in four sets. The users in the cells center zones use the same subset of subcarriers whereas the users of the border zones use the remaining resources with a Fractional Frequency Reuse (FFR) equal to 3. Considering the same cell zones, we present in this work a different FFR model and prove with a theoretical analysis that the system throughput can be improved. The resource allocation is then performed considering the Weighted Proportional Fair (WPF) algorithm. Simulation results illustrate the performance improvements of the proposed solution in comparison to the classical model. The fairness for the different models is also studied and compared.

In this paper, we propose a transceiver structure for the multi-user MIMO uplink channel of the Long Term Evolution (LTE) networks. This transceiver structure is based on the use of an adequate encoder at the transmitter side, to take advantage of the multiple transmit antenna diversity, and a combination of a joint zero forcing (ZF) and a maximum likelihood (ML) decoder at the receiver side. Based on this transceiver design, we show how to extend an opportunistic multiuser spatial multiplexing allocation to the whole LTE bandwidth with respect to Single Carrier Frequency Division Multiple Access (SC-FDMA) constraints. The evaluated rate performances of the proposed transceiver in a scheduling uplink LTE context outperform the ones of other well known decoders.

We investigate in this paper the resource allocation problem in an uplink OFDMA multiuser system. We focus on the Proportional Fairness approach which aims to maximize the system throughput and while ensuring fairness between users. In this paper, we propose a Weighted Proportional Fair algorithm for resource allocation that introduces priorities between users to better exploit good channel conditions in the cell center. We also perform a theoretical analysis to compare both standard and weighted proportional fair algorithms. Simulation results, compared to existing algorithms, show that the proposed algorithm approaches the max sum rate upper bound but still maintains sufficient fairness between users.

This work proposes two new Resource Block (RB) allocation algorithms for the LTE uplink. They take into account the RB adjacency constraint and update the allocation metric. Two different heuristics are proposed: an Opportunistic and Efficient RB allocation Algorithm (OEA) and a Quality of Service based Opportunistic and Efficient RB allocation Algorithm (QoS based OEA). Both algorithms seek to maximize the aggregate throughput and avoid RB wastage unlike other algorithms in the literature. The complexity of the proposed algorithms are also computed analytically and compared to other well known heuristics.

In the next generation of multicell networks, adaptive resource allocation techniques will play an important role to improve both quality of service and spectral efficiency. In order to employ distributed power allocation for multicell Orthogonal Frequency-Division Multiple Access (OFDMA) networks, the channel state information (CSI) belonging to all users is required to share among base stations. However, the amount of feedback increases with the number of users, base stations and subcarriers. Therefore, it is important to perform a selection at the user side for multicell networks. In this paper, we propose reduced feedback links by choosing the users based on their approximate signal to interference noise ratio (SINR). The performance of the reduced feedback links are illustrated in multicell OFDMA systems.

This paper addresses a power control and Inter-Cell Interference (ICI) estimation problem in uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) networks. We consider a power control based on Signal to Interference plus Noise Ratio (SINR) target to be achieved, which requires knowledge of the interference level. We propose a simple method to estimate ICI in uplink SC-FDMA networks, less greedy than the classical Monte Carlo method which has higher computation complexity. Numerical results validate the new method in multiple environments even after adding fast fading and shadowing effect. Moreover, it shows robustness against variation of above mentioned parameters.

This paper proposes a general method for bi-directional relaying with three nodes over four time intervals, composed of alternate MAC phases and broadcast phases. Different nodes transmit in MAC phases, and the data stored in broadcast phases allows decoding of all information. The physical layer network coding strategy for this relaying protocol is a lattice-based coding scheme combined with power control, so that the relay receives an integer linear combination of the nodes codewords. This strategy is analyzed for MIMO with 2 receive and transmit antennas. Analytical expressions of the exchange rate and transmit powers under high SNR approximation are provided, and numerically validated with respect to the general SNR mode. The proposed method is then compared with Amplify-and-Forward. It is shown that lattice-based coding provides a gain of 4.5 dB in average SNR on the achievable exchange rate, and is totally fair, contrary to Amplify-and-Forward.

This paper addresses multiple access in MIMO wireless networks. It compares two distributed interference alignment techniques, in space and in frequency, that both aim at removing interferences through orthogonalization. The theoretical advantages and drawbacks of each technique are highlighted. Then, the multiplexing gain and the computational complexity are analytically evaluated. It is shown that frequency interference alignment achieves a higher multiplexing gain than space interference alignment in realistic network conditions, and always requires a lower computational complexity. Finally, the performances of both algorithms with varying number of interfering links are assessed via numerical simulations. Space interference alignment fulfills complete interference suppression, and consequently provides the same spectral efficiency to all links, regardless of the number of interfering links. Frequency interference alignment is less efficient in terms of INR, and the spectral efficiency per link thus decreases when the number of links increases. Nevertheless, the spectral efficiency is always higher with frequency interference alignment than with space interference alignment.

This paper addresses the Margin Adaptive (MA) problem in MIMO OFDMA multi-cell networks. Resource allocation is distributed within each Base Station. Two cases are considered: full CSIT, and statistical CSIT. In both cases, a distributed convergence criterion for power control is determined and included within iterative power allocation. In the statistical CSIT case, an approximate analytical expression is obtained for the outage capacity, as a function of the SNR and the outage probability. It turns the MA problem into a convex optimization problem, and is valid at practical SNR and outage capacity values, for several antenna configurations. The proposed methods avoid power divergence situations at any load, and are consequently more efficient than iterative water-filling to solve the MA problem.

This paper addresses the weighted sum throughput maximization problem on the interference channel. It proposes a sub-optimal algorithm that enables to perform a distributed power allocation, which performs well at any SINR regime. The sub-optimal method consists of two phases: first, identify the links that should be set to zero, and then, operate in the high SINR regime for the remaining links. In the single channel case, this simple, fully distributed algorithm is shown to perform almost as well as the centralized optimal algorithm. In the multi-channel case, the algorithm is more efficient than iterative water-filling in order to decrease the buffer size.

This paper addresses inter-cell interference mitigation techniques in OFDMA cellular networks. It compares the performance results obtained with partial frequency reuse in the outer cell and with a proposed cooperative scheme based on relaying of the users in the outer cell. The proposed scheme uses cooperative diversity via a relaying protocol between the source base station and the closest base station, therefore reducing inter-cell interference. Subcarrier and power allocation can be implemented in a distributed way in each base station. Numerical results show that the same average inter-cell interference is achieved with cooperation as with a frequency reuse factor of 3, whereas cooperation leads to a lower rejection probability and higher sum throughput at medium to high load.

Cooperative diversity at a system level is a promising method to improve the performance of the users located at the border of cells in distributed cellular networks. In this paper, we propose a Quality-of-Service based resource allocation algorithm for downlink OFDMA, in which Base Stations trigger cooperative diversity for users requiring a minimum data rate. As Downlink cooperation increases the spectral efficiency for users at the border of the cells, it enables to decrease the power required for these users. The remaining Best Effort users can then be served with more power, therefore increasing their sum throughput. The proposed distributed method is compared with other resource allocation techniques, and proves to be very efficient for the users with both types of Quality of Service.

This paper addresses downlink cooperation at a system level. Cooperation between base stations is an alternative to macrodiversity to provide QoS continuity in case of mobility. We propose a radio resource management strategy for the relay channel, made of relayed users identification, resource allocation and power control in an OFDMA-based system. Four scheduling methods are tested for power allocation in inter-cell interference environment: globally optimal, proportional fair, harmonic mean fair and max-min fair. Cooperation brings additional gain, both in terms of throughput and fairness, with all fair schedulers.

Downlink cooperation between base stations is a simple, efficient alternative to macrodiversity for providing QoS continuity during mobility in distributed cellular networks. It brings cooperation diversity and only requires data forwarding between base stations. This paper presents a strategy for downlink cooperation triggering, resource allocation and power control in an OFDMA-based system. Power control aims at maximizing the equivalent capacity that is achieved thanks to the diversity brought by relaying, while taking into account inter-cell interference. We use an iterative method which dedicates a proportion of the total power to relayed users. Simulation results show that our method increases capacity at any load. It importantly reduces the rejection probability thanks to SIR increase of users at cell's border. The paper also shows that cooperation should be limited to cell-border users for restricting additional resource consumption, and that it should make use of diversity as much as possible.

In cellular networks, base stations can cooperate in order to bring spatial diversity. Cooperative diversity enables to increase link capacity and signal to noise ratio. This paper presents source and relay base station's optimal power allocation in order to maximize the sum capacity over all relayed links. Average power allocation is solved by an iterative method which allocates relay and source powers separately, by convex optimization. Then source symbol powers are set with simple water-filling, in order to maximize the capacity. Simulation results show that sum capacity is highly increased by relaying under optimal power values. Relaying is especially efficient for user terminals who would have poor radio conditions without relaying. The paper also presents a simple method, based on path loss characterization, to determine the need for relaying. It can be used independently in the source base stations, which is particularly adapted for flat architectures.

Most handover algorithms between UMTS and WLAN networks require coverage information obtained by mobile terminal measurements. However, nothing is currently forecast in standards to ease these potentially costly measurements. We therefore propose a handover probability database used to compute, for each mobile terminal, the identity of the most probable UMTS cells and WLAN Access Points for inter-system handover. Handover probability database results may be either one element for blind handover, or a neighboring list to limit mobile terminal's measurements to the most probable cells or Access Points. The method is assessed with a dynamic simulation tool. Blind handover from UMTS to WLAN using the handover probability database shows good performance, compared with direct blind handover. The neighboring cell list size is importantly restricted, in both directions. The handover probability database can be combined with handover or reselection algorithms, at different levels of UMTS -WLAN inter- working architecture.

Mobile terminals measurements capabilities are strong limitations that may impede seamless handover from UMTS to WLAN. In this article, we propose an inter-system handover method that does not require in-line measurements on WLAN. It identifies the most probable WLAN Access Point for handover, from coverage point of view, depending on UMTS signal measurements and on a handover probability database. This method can be implemented in the RNC, or in a B3G common controller. Simulation results in a WLAN hot spot deployment show that handover based on a priori knowledge of the networks increases handover success rate, compared to classical blind handover. Its performance can be improved by optimizing the handover probability database. Besides, the proposed method enables to forecast handover's success, and adapt accordingly. Handover based on a priori knowledge of the networks can consequently be integrated into several B3G handover algorithms, to decrease handover delay and complexity.