Marco Moretti

A Resource Allocator for the Uplink of Multi-Cell OFDMA Systems

We propose a simple distributed radio resource allocation algorithm for an OFDMA cellular system, which aims at minimizing the overall transmitted power subject to a rate constraint for each user. In order to reduce the problem complexity we use a single modulation; simulations show that the resulting performance degradation is negligible when the number of users is high enough. Moreover, we propose a simple distributed heuristic that, by reducing the rate constraints, steers the multicell system towards an stable resource allocation. Results show that the proposed system exhibits a great robustness to the destructive effects of multiple access interference.

Centralized Radio Resource Allocation for OFDMA Cellular Systems

Efficient resource allocation in cellular OFDMA systems envisages the assignment of the number of sub-carriers and the relative transmission format on the basis of the experimented link quality. In this way, a higher number of sub-carriers with low per-carrier cshould be assigned to users at cell border. This strategy has already proved its efficiency in the single-cell scenario, while no study has been provided in the multi-cell scenario with reuse factor equal to one, i.e., in presence of severe interference conditions. In this paper we propose an optimum centralized radio resource allocator for the multi-cell scenario of an OFDMA cellular system which allows to highly outperform iterative decentralized allocation strategies based on local optimization criteria. The proposed scheme is characterized by huge implementation complexity, and, hence, it can be hardly implemented in the real world. However, it can help the system designer in catching the essence of interference limitations in OFDMA cellular systems, thus allowing the elaboration of efficient heuristic decentralized approaches. As an example, we prove that the sub-carrier transmission format adaptation is not useful in a multi-cell scenario. This is because users at cell border tends to consume the most of the resources (i.e., they are assigned the most of sub-carriers), thus producing interference for the neighbor cells over a large set of sub-carriers. Hence, since in this case neighbor cells are forced to use those (few) sub-carriers which experiment low interference, the diversity gain tends to be missed.

Fine carrier and sampling frequency synchronization in OFDM systems

This paper investigates the joint pilot-assisted estimation of the residual carrier frequency offset (RCFO) and sampling frequency offset (SFO) in an orthogonal frequency division multiplexing (OFDM) system. As it is known, the exact maximum-likelihood (ML) solution to this problem involves a bidimensional grid-search that cannot be pursued in practice. After introducing an enlarged set of auxiliary unknown parameters, however, the RCFO and SFO recovery tasks can be decoupled and the bidimensional search is thus replaced with a simpler mono-dimensional search. This results into an estimation algorithm of reasonable complexity which is suitable for practical implementation. To further reduce the processing load, we also present an alternative scheme yielding frequency estimates in closed-form. Numerical simulations indicate that the proposed methods outperform existing estimators available in the literature in terms of both estimation accuracy and error-rate performance.

Channel estimation in OFDM systems with unknown interference

We investigate the problem of channel estimation in an orthogonal frequency-division multiplexing (OFDM) system plagued by unknown narrowband interference (NBI). Such scenario arises in many practical contexts, including cellular applications and emerging spectrum sharing systems, where coexistence of different types of wireless services over the same frequency band may result into remarkable co-channel interference. Estimation algorithms devised for conventional OFDM transmissions are expected to suffer from significant performance degradation in the presence of NBI. To overcome this difficulty, in the present work we follow a novel pilot-aided approach where the interference power on each pilot subcarrier is treated as a nuisance parameter which is averaged out from the corresponding likelihood function. The latter is then maximized in an iterative fashion according to the expectation-maximization (EM) principle or by applying the Jacobi-Newton algorithm. The resulting schemes have affordable complexity and are inherently robust to NBI. Their accuracy is investigated by means of computer simulations and compared with the relevant Cramer- Rao bound.

Integer frequency offset recovery in OFDM transmissions over selective channels

Carrier frequency offset (CFO) in OFDM systems is normally estimated in two steps. The fractional part of the CFO is recovered first and the remaining ambiguity is subsequently resolved by detecting the integer frequency offset (IFO). Conventional IFO recovery algorithms for OFDM signals are sensitive to multipath distortions as they are derived without explicitly taking into account the frequency selectivity of the transmission channel. In this paper, we propose a novel scheme in which the channel response and IFO are jointly estimated using a maximum likelihood (ML) approach. In doing so we exploit one or more pilot blocks placed at the beginning of the frame and carrying known symbols. Since the complexity of the resulting ML algorithm may be relatively large, we also suggest suboptimal solutions unifying various earlier proposals. Computer simulations are used to demonstrate the superiority of the proposed schemes over existing alternatives. It is shown that excellent performance can be achieved with affordable complexity even in the presence of highly dispersive channels.

Efficient Margin Adaptive Scheduling for MIMO-OFDMA Systems

In this paper we address the problem of margin adaptive scheduling in the downlink of an orthogonal frequency division multiple access (OFDMA) multiple-input multiple-output (MIMO) system. Optimal resource allocation in MIMO systems requires the joint optimization of: a) linear transmit and receive spatial filters, b) channel assignment and c) power allocation. This problem is not convex and its complexity becomes thus intractable already for small sets of users and subcarriers. To reduce the complexity of the problem at hand, we propose a novel heuristic strategy that partitions the users in different groups according to their average channel quality and addresses the original problem by solving a succession of lower-complexity allocation problems. The spatial dimension is employed to prevent multiple access interference from hindering the performance of the sequential allocation. To further reduce the complexity burden we introduce a linear programming formulation in combination with a waterfilling-based strategy to allocate channels and power to the groups of users. Numerical results and evaluation of the computational complexity show that, though suboptimal, in most cases the proposed algorithm manages to exploit in an original way the inherent multi-user diversity of multi-carrier systems to ease the task of resource allocation with a very limited performance loss from the theoretic optimum.

Radio resource allocation problems for OFDMA cellular systems

Orthogonal frequency division multiple-access (OFDMA) manages to efficiently exploit the inherent multi-user diversity of a cellular system by performing dynamic resource allocation. Radio resource allocation is the technique that assigns to each user in the system a subset of the available radio resources (mainly power and bandwidth) according to a certain optimality criterion on the basis of the experienced link quality. In this paper we address the problem of resource allocation in the downlink of a multi-cellular OFDMA system. The allocation problem is formulated with the goal of minimizing the transmitted power subject to individual rate constraint for each user. Exact and heuristic algorithms are proposed for the both the single-cell and the multi-cell scenario. In particular, we show that in the single-cell scenario the allocation problem can be efficiently solved following a network flow approach. In the multi-cell scenario we assume that all cells use the same frequencies and therefore the allocation problem is complicated by the presence of strong multiple access interference. We prove that the problem is strongly NP-hard, and we present an exact approach based on an MILP formulation. We also propose two heuristic algorithms designed to be simple and fast. All algorithms are tested and evaluated through an experimental campaign on simulated instances. Experimental results show that, although suboptimal, a Lagrangian-based heuristic consisting in solving a series of minimum network cost flow problems is attractive for practical implementation, both for the quality of the solutions and for the small computational times.

Robust frequency synchronization for OFDM-based cognitive radio systems

Cognitive radio employs spectrum sensing to facilitate coexistence of different communication systems over a same frequency band. A peculiar feature of this technology is the possible presence of interference within the signal bandwidth, which considerably complicates the synchronization task. This paper investigates the problem of carrier frequency estimation in an orthogonal frequency-division multiplexing (OFDM)-based cognitive radio system that operates in the presence of narrowband interference (NBI). Synchronization algorithms devised for conventional OFDM transmissions are expected to suffer from significant performance degradation when the received signal is plagued by NBI. To overcome this difficulty, we propose a novel scheme in which the carrier frequency offset (CFO) and interference power on each subcarrier are jointly estimated through maximum likelihood (ML) methods. In doing so we exploit two pilot blocks. The first one is composed of several repeated parts in the time-domain and provides a CFO estimate which may be affected by a certain residual ambiguity. The second block conveys a known pseudo-noise sequence in the frequency-domain and is used to resolve the ambiguity. The performance of the proposed algorithm is assessed by simulation in a scenario inspired by the IEEE 802.11g WLAN system in the presence of a Bluetooth interferer.

A Layered Architecture for Fair Resource Allocation in Multicellular Multicarrier Systems

We consider a multicell multicarrier system with frequency reuse distance that is equal to one. Allowing all cells to transmit on the whole bandwidth unveils large potential gains in terms of spectral efficiency, in comparison with conventional cellular systems. Such a scenario, however, is often deemed unfeasible because of the strong multiple access interference (MAI) that negatively affects system performance. This paper presents a layered architecture that integrates a packet scheduler with an adaptive resource allocator that was explicitly designed to take care of the MAI. Each cell performs its resource management in a distributed way with no central controller. Iterative resource allocation assigns radio channels to the users to minimize interference. Packet scheduling guarantees that all users get a fair share of resources, regardless of their position in the cell. This scheduler-allocator architecture integrates both goals and is able to self-adapt to any traffic and user configuration. An adaptive distributed load control strategy can reduce the cell load so that the iterative procedure always converges to a stable allocation, regardless of the interference. Numerical results show that the proposed architecture guarantees both high spectral efficiency and throughput fairness among flows.

Carrier Frequency Offset Estimation for OFDM Direct-Conversion Receivers

We investigate the problem of carrier frequency offset (CFO) recovery in an OFDM direct-conversion receiver plagued by both dc-offset and frequency-selective I/Q imbalance. In order to enlarge the frequency acquisition range, the CFO is divided into an integer part, which is multiple of the subcarrier spacing, plus a remaining fractional part. The fractional CFO is firstly estimated by resorting to the least-squares (LS) principle using a suitably designed training sequence. Since the exact LS solution requires a complete search over the frequency uncertainty range, we propose a simpler scheme that dispenses from any peak-search procedure. We also derive an approximated closed-form expression of the estimation accuracy that reveals useful for assessing the impact of various design parameters on the system performance. After computing the fractional CFO, the integer frequency error is eventually retrieved by following a weighted LS approach. Numerical simulations and theoretical analysis indicate that the proposed scheme can be used to obtain accurate CFO estimates with affordable complexity.