Proactive Location-Based Scheduling of Delay-Constrained Traffic Over Fading Channels

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.Comment: Conference: VTC2016-Fall, At Montreal-Canad

Optimum Transmission Through the Multiple-Antenna Gaussian Multiple Access Channel

"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."[EN] This paper studies the optimal points in the capacity region of Gaussian multiple access channels (GMACs) with constant fading, multiple antennas, and various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing the timesharing parameters, the input covariance matrices, and the order of decoding used by the successive interference cancellation receiver. To approach this problem, Carathéodory s theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear rate objectives to more general, potentially nonconvex, problems, and to obtain a necessary and sufficient condition for the optimality of the transmission parameters in a wide range of problems. Using the insights gained from this condition, we develop and analyze the convergence of an algorithm for solving, otherwise daunting, GMAC-based optimization problems.D. Calabuig was supported by Marie Curie International Outgoing Fellowship within the European Commission, Research Executive Agency, through the COMIC Project under Grant 253990. R. H. Gohary and H. Yanikomeroglu were supported in part by Huawei Canada Company, Ltd., and in part by the Ontario Ministry of Economic Development and Innovations within the Ontario Research Fund through the Research Excellence Program. This paper was presented at the 2013 IEEE International Symposium on Information Theory and the 2014 IEEE International Workshop on Signal Processing Advances in Wireless Communications.Calabuig Soler, D.; Gohary, RH.; Yanikomeroglu, H. (2016). Optimum Transmission Through the Multiple-Antenna Gaussian Multiple Access Channel. IEEE Transactions on Information Theory. 62(1):230-243. https://doi.org/10.1109/TIT.2015.2502244S23024362

Hierarchical coherent and non-coherent communication

[abstract not available]https://fount.aucegypt.edu/faculty_book_chapters/1213/thumbnail.jp

Optimal Power Assignment for MIMO Channels Under Joint Total and Per-Group Power Constraints

In this paper we consider a communication system with one transmitter and one receiver. The transmit antennas are partitioned into disjoint groups, and each group must satisfy an average power constraint in addition to the standard overall one. The optimal power allocation (OPA) for the transmit antennas is obtained for the following cases: (i) fixed multiple-input multiple-output (MIMO) orthogonal channel, (ii) i.i.d. fading MIMO orthogonal channel, and (iii) i.i.d. Rayleigh fading multiple-input single-output (MISO) and MIMO channels. The channel orthogonality is encountered in the practical case of the massive MIMO channel under favorable propagation conditions. The closed-form solution to the OPA for a fixed channel is found using the Karush-Kuhn-Tucker (KKT) conditions and it is similar to the standard water-filling procedure while the effect of the per-group average power constraint is added. For a fading channel, an algorithm is proposed to give the OPA, and the algorithm's convergence is proved via a majorization inequality and a Schur-concavity property

Optimization of Discrete Power and Resource Block Allocation for Achieving Maximum Energy Efficiency in OFDMA Networks

Most of the resource allocation literature on the energy-efficient orthogonal frequency division multiple access (OFDMA)-based wireless communication systems assume continuous power allocation/control, while, in practice, the power levels are discrete (such as in 3GPP LTE). This convenient continuous power assumption has mainly been due to either the limitations of the used optimization tools and/or the high computational complexity involved in addressing the more realistic discrete power allocation/control. In this paper, we introduce a new optimization framework to maximize the energy efficiency of the downlink transmission of cellular OFDMA networks subject to power budget and quality-of-service constraints, while considering discrete power and resource blocks (RBs) allocations. The proposed framework consists of two parts: 1) we model the predefined discrete power levels and RBs allocations by a single binary variable and 2) we propose a close-to-optimal semidefinite relaxation algorithm with Gaussian randomization to efficiently solve this non-convex combinatorial optimization problem with polynomial time complexity. We notice that a small number of power levels suffice to approach the energy efficiency performance of the continuous power allocation. Based on this observation, we propose an iterative suboptimal heuristic to further reduce the computational complexity. Simulation results show the effectiveness of the proposed schemes in maximizing the energy efficiency, while considering the practical discrete power levels

Enabling Sphere Decoding for SCMA

In this paper, we propose a reduced-complexity optimal modified sphere decoding (MSD) detection scheme for SCMA. As SCMA systems are characterized by a number of resource elements (REs) that are less than the number of the supported users, the channel matrix is rank-deficient, and sphere decoding (SD) cannot be directly applied. Inspired by the Tikhonov regularization, we formulate a new full-rank detection problem that it is equivalent to the original rank-deficient detection problem for constellation points with constant modulus and an important subset of non-constant modulus constellations. By exploiting the SCMA structure, the computational complexity of MSD is reduced compared with the conventional SD. We also employ list MSD to facilitate channel coding. Simulation results demonstrate that in uncoded SCMA systems the proposed MSD achieves the performance of the optimal maximum likelihood (ML) detection. Additionally, the proposed MSD benefits from a lower average complexity compared with MPA.Comment: Accepted for publication in IEEE Communications Letter