Mohamad Assaad

Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility Maximization Under Imperfect Channel-State Information

We consider the problem of simultaneous user-scheduling, power-allocation, and rate-selection in an orthogonal frequency division multiple access (OFDMA) downlink, with the goal of maximizing expected sum-utility under a sum-power constraint. In doing so, we consider a family of generic goodput-based utilities that facilitate, e.g., throughput-based pricing, quality-of-service enforcement, and/or the treatment of practical modulation-and-coding schemes (MCS). Since perfect knowledge of channel state information (CSI) may be difficult to maintain at the base-station, especially when the number of users and/or subchannels is large, we consider scheduling and resource allocation under imperfect CSI, where the channel state is described by a generic probability distribution. First, we consider the “continuous” case where multiple users and/or code rates can time-share a single OFDMA subchannel and time slot. This yields a nonconvex optimization problem that we convert into a convex optimization problem and solve exactly using a dual optimization approach. Second, we consider the “discrete” case where only a single user and code rate is allowed per OFDMA subchannel per time slot. For the mixed-integer optimization problem that arises, we discuss the connections it has with the continuous case and show that it can solved exactly in some situations. For the other situations, we present a bound on the optimality gap. For both cases, we provide algorithmic implementations of the obtained solution. Finally, we study, numerically, the performance of the proposed algorithms under various degrees of CSI uncertainty, utilities, and OFDMA system configurations. In addition, we demonstrate advantages relative to existing state-of-the-art algorithms.

Optimal Fractional Frequency Reuse (FFR) and resource allocation in multiuser OFDMA system

In this paper we determine the optimal Fractional Frequency Reuse (FFR) and resource allocation in OFDMA system. Since the users at the cell edge are more exposed to inter-cell interference therefore each cell is partitioned into two regions; inner region and outer region. We determine the optimal FFR factor for the outer region, bandwidth assigned to each region and subcarrier and power allocation to all the users in the cell. The problem is formulated as sum-power minimization problem subject to minimum rate constraints in both the regions. This is a mixed linear integer programing problem which is relaxed into a convex optimization problem. We develop an efficient algorithm by using Lagrange dual decomposition theory at reasonable computational cost.

New Frequency-Time Scheduling Algorithms for 3GPP/LTE-like OFDMA Air Interface in the Downlink

This paper proposes new frequency-time schedulers for 3GPP/LTE-like system in the downlink. 3GPP/LTE DL is OFDMA-based with adaptive modulation and coding (AMC) and frequency-time scheduling to enhance spectral efficiency and aggregate system throughput. Time-frequency scheduling and AMC can be implemented jointly or separately i.e. sequentially AMC after scheduling. Two novel schedulers are proposed in this paper with respectively joint and separate implementation of scheduling and AMC. Through system level performance evaluation, results show that the first scheduler with separate AMC and scheduling implementation outperforms the second scheduler (with joint AMC implementation) in terms of trade off between fairness and capacity and implementation complexity.

Asymptotic analysis of distributed multi-cell beamforming

We consider the problem of multi-cell downlink beamforming with N cells and K terminals per cell. Cooperation among base stations (BSs) has been found to increase the system throughput in a multi-cell set up by mitigating inter-cell interference. Most of the previous works assume that the BSs can exchange the instantaneous channel state information (CSI) of all their user terminals (UTs) via high speed backhaul links. However, this approach quickly becomes impractical as N and K grow large. In this work, we formulate a distributed beamforming algorithm in a multi-cell scenario under the assumption that the system dimensions are large. The design objective is the minimize the total transmit power across all BSs subject to satisfying the user SINR constraints while implementing the beamformers in a distributed manner. In our algorithm, the BSs would only need to exchange the channel statistics rather than the instantaneous CSI. We make use of tools from random matrix theory to formulate the distributed algorithm. The simulation results illustrate that our algorithm closely satisfies the target SINR constraints when the number of UTs per cell grows large, while implementing the beamforming vectors in a distributed manner.

Low complexity margin adaptive resource allocation in downlink MIMO-OFDMA system

We study the downlink multiuser Multiple Input Multiple Output-Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) system for margin adaptive resource allocation where the base station (BS) has to satisfy individual quality of service (QoS) constraints of the users subject to transmit power minimization. Low complexity solutions involve beamforming techniques for multiuser inter-stream interference cancellation. However, when beamforming is introduced in the margin adaptive objective, it becomes a joint beamforming and resource allocation problem. We propose a sub-optimal two-step solution which decouples beamforming from subcarrier and power allocation. First a reduced number of user groups are formed and then the problem is formulated as a convex optimization problem. Finally an efficient algorithm is developed which allocates the best user group to each subcarrier. Simulation results reveal comparable performance with the hugely complex optimal solution.

Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach

We consider the problem of coordinated multicell downlink beamforming in massive multiple input multiple output (MIMO) systems consisting of N cells, Nt antennas per base station (BS) and K user terminals (UTs) per cell. In particular, we formulate a multicell beamforming algorithm for massive MIMO systems that requires limited amount of information exchange between the BSs. The design objective is to minimize the aggregate transmit power across all the BSs subject to satisfying the user signal-to-interference-noise ratio (SINR) constraints. The algorithm requires the BSs to exchange parameters which can be computed solely based on the channel statistics rather than the instantaneous channel state information (CSI). We make use of tools from random matrix theory to formulate the decentralized algorithm. We also characterize a lower bound on the set of target SINR values for which the decentralized multicell beamforming algorithm is feasible. We further show that the performance of our algorithm asymptotically matches the performance of the centralized algorithm with full CSI sharing. While the original result focuses on minimizing the aggregate transmit power across all the BSs, we formulate a heuristic extension of this algorithm to incorporate a practical constraint in multicell systems, namely the individual BS transmit power constraints. Finally, we investigate the impact of imperfect CSI and pilot contamination effect on the performance of the decentralized algorithm, and propose a heuristic extension of the algorithm to accommodate these issues. Simulation results illustrate that our algorithm closely satisfies the target SINR constraints and achieves minimum power in the regime of massive MIMO systems. In addition, it also provides substantial power savings as compared with zero-forcing beamforming when the number of antennas per BS is of the same orders of magnitude as the number of UTs per cell.

Cross-Layer design in HSDPA system to reduce the TCP effect

This paper focuses on the interaction between the transport control protocol (TCP) layer and the radio interface in the high-speed downlink packet access (HSDPA) wireless system. In the literature, studies of the interaction between TCP and wireless networks are focused on the evaluation of user bit rate in the case of dedicated channels. In this paper, the interaction between TCP, hybrid automatic repeat request (HARQ), and scheduling techniques (especially, proportional fair scheduling) is conducted. Analytical models to evaluate HSDPA cell capacity, user bit rate, and interaction with TCP layer are presented. Even if as expected the bit rate per flow decreases strongly with the congestion frequency in the wired network, it is shown that the overall capacity achieved by HSDPA is not as affected by the TCP layer. Using this result, a method to reduce the effect of TCP on wireless network without losing much cell capacity is proposed. This method has the advantage of modifying the scheduling algorithm only and of not requiring any change to the TCP protocol.

On the capacity of HSDPA

An analytical model for the evaluation of HSDPA capacity is proposed and used to carry out a comparison of several MIMO systems. HSDPA is an evolution of the UMTS standard over the air interface to achieve higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and Blast) techniques. The model is used to compare MIMO systems, a typically difficult task to provide some insight. The results are supported by a simulation.

Polynomial-Complexity Optimal Resource Allocation Framework for Uplink SC-FDMA Systems

In this paper, we study a weighted-sum rate maximization problem in SC-FDMA which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel polynomial-complexity optimization framework that is inspired from the recently developed canonical duality theory. We first formulate the resource allocation problem as a binary-integer programming (BIP) problem and then transform the BIP problem into a continuous space canonical dual problem which is a concave maximization problem. Based on the solution of the continuous space dual problem, we propose a resource allocation algorithm that has polynomial complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature to assess its performance. Simulation results show that the proposed algorithm improves the system performance significantly.

Downlink B3G MIMO OFDMA Link and System Level Performance

This paper provides a link and system level study of the downlink in a B3G system using MIMO OFDMA techniques. A 3GPP/LTE-like scenario, in terms of frame structure and system main parameters, is considered in this study. At the link level, the double Alamouti MIMO scheme is studied and the performances of various modulation and coding schemes (MCS) are provided. The impact of real MIMO channel estimation (including RF impairments, CFO compensation and automatic gain control AGC) on the MCSs link performance is also presented. System level study is thus carried out to assess the performance of the downlink OFDMA in terms of coverage and cell throughput. Several scheduling disciplines and MIMO schemes are investigated and the impact of the spatial diversity on the cell throughput is provided. This study has been carried out in the scope of the French RNRT OPUS project.