Daniel Castanheira

Distributed antenna system capacity scaling [Coordinated and Distributed MIMO]

The distributed antenna system concept promises to enhance the capacity and diversity of next-generation wireless communication networks, due to the inherently added micro and macro diversity. In this article we first give an overview of the main benefits of a DAS in relation to a collocated antenna system. Next we study the sum-capacity scaling of a multi-user DAS with the number of jointly processed transmit antennas in the downlink. In a practical system this scaling will have implications on the number of antennas worth jointly processing, since the costs of processing an additional antenna can be higher than the additional benefits obtained. Results show that the most important system property to attain the highest capacity gains is symmetry, and the users that attain the maximum gain are those at cell borders. They also confirm that the main DAS feature that makes possible its gains over the CAS architecture are the additional degrees of freedom/diversity provided by such an architecture, which increase the probability of finding a system state with high symmetry and of each user being near one of the transmit antennas.

Set Optimization for Efficient Interference Alignment in Heterogeneous Networks

To increase capacity and offload traffic from the current macro-cell cellular system, operators are considering the deployment of small cells. It is expected that both the small and macro-cells will coexist in the same spectrum, resulting in unsustainable levels of interference. Interference alignment is considered as an effective method to deal with such interference. By using interference alignment, the small cells align their transmission along a common direction to allow the macro-cell receiver to completely remove it. It is clear that, if the two systems have no limitations on the information that may be exchanged between them to perform the signal design, then the performance may be improved in comparison to the case of no or partial cooperation. However, this full-cooperation strategy requires a high-rate connection between the macro and small cells, which may not be available. To overcome this problem, we consider that the alignment direction is selected from a finite set, known to both macro- and small-cell terminals. We provide sufficient conditions for this set that guarantee full diversity, at the macro-cell, and propose an efficient method to optimize the set elements. Results show that an alignment set with a description length of 1 bit is enough to achieve the same diversity as in the case where an infinite amount of information is exchanged between both systems. The proposed set optimization method achieves better performance than random vector quantization and similar performance to Grassmannian quantization.

Novel Windowing Scheme for Cognitive OFDM Systems

In this paper a new shaping window is designed to force the side-lobe of OFDM transmission to decay faster than conventional raised-cosine windowing. The proposed window is a soft-window based on the characteristics of functions with vestigial symmetry, which are common in digital data transmission. By using the vestigial symmetry and enforcing several null derivatives at the extremes of the window, it is possible to control the fast out-of-band decay. Results show that by using a lower roll-off factor (10%) the proposed window can achieve the same performance of raised-cosine (100 %) with around 90% overhead savings, making it of interest for OFDM cognitive radio.

Efficient Transmitter and Receiver Designs for SC-FDMA Based Heterogeneous Networks

In this manuscript, we consider the uplink of a single carrier frequency division multiple access (SC-FDMA) based system, where a set of small-cells coexist with a macro-cell using the same spectrum. To deal with both the inter-carrier interference (ICI) and inter-system interference, we combine interference alignment (IA) precoding at the small-cell transmitters with iterative decision feedback equalization at the macro-receiver. The transmitter and receiver design is performed jointly by considering both full-coordination and limited inter-system information exchange between macro- and small-cells. At the small cells side, we assume that the access points are connected through a limited capacity backhaul network to a central unit, where the separation of a quantized version of the small-cell signals is performed. For this case, the iterative feedback equalizer is designed by explicitly taking into account the impact of signal quantization. Moreover, a semi-analytical approach for the performance of the proposed schemes is provided. The results show that the proposed schemes are robust to both inter-system and inter-carrier interferences and thus are able to efficiently separate the macro- and small-cells spatial streams under limited information exchange.

Lossy source coding using belief propagation and soft-decimation over LDGM codes

This paper focus on the lossy compression of a binary symmetric source. We propose a new algorithm for binary quantization over low density generator matrix (LDGM) codes. The proposed algorithm is a modified version of the belief propagation (BP) algorithm used in the channel coding framework and has linear complexity in the code block length. We also provide a common framework under which the proposed algorithm and some previously proposed algorithms fit. Simulation results show that our scheme achieves close to state-of-the-art performance with reduced complexity.

Null-space cognitive precoding for heterogeneous networks

Small-cells are considered as an effective solution to increase capacity and offload traffic from, the current macro-cell cellular system. Owing to the difficulty and costs involved in acquiring new spectrum licenses, small-cells are expected to coexist with their respective macro-cells, in the same spectrum. This leads to considerable interference between the two systems. Optimum performance, at the macro-cell, is achieved when the small-cell terminals transmit their information over the null-space of the macro-cell link. However, availability, at the small-cell terminals, of the macro-cell channel null-space information requires full-cooperation and thus a high overhead of information exchange. In this study, the cognitive precoding schemes are designed under a limited inter-system information exchange and the constraint that the performance of the macro-cell link is kept close to the case where no small-cell network does exist. Two techniques are considered: a two-bit quantisation precoded and a dual space-frequency coding precoded approach. It is demonstrated that the first achieves a performance close to the full cooperation approach recently proposed, but with very low information exchange requirements. For the second, it is show that both the systems are able to coexist without any inter-system cooperation and with a performance close to the non-coexistence scenario.

Minimum bit error rate nonlinear precoding for multiuser MIMO and high SNR

Abstract This manuscript focuses on the minimization of the bit-error-rate in the high signal to noise ratio regime, for the downlink of a multiuser MIMO channel with N transmit antennas and K single antenna users. In the design of such a precoder the knowledge of the transmitted data and full channel state information at the transmitter are assumed. It is shown that, in the high signal to noise regime, the problem simplifies from a constrained quadratic nonlinear optimization to a single quadratic program, allowing to reduce the complexity. This quadratic problem is equivalent to maximize the minimum distance between the user received symbols and corresponding decision boundaries. The proposed algorithm selects and inverts part of the correlation matrix, unlike the zero-forcing where full inversion is required. This leads to a better performance as the selection allows us to get a better conditioned matrix. Also, this allows us to treat zero-forcing as a special case of the algorithm. The results show that the algorithm achieves a performance close to the optimum, with much lower complexity.

A novel distributed power allocation scheme for coordinated multicell systems

Coordination between base stations (BSs) is a promising solution for cellular wireless systems to mitigate intercell interference, improving system fairness, and increasing capacity in the years to come. The aim of this manuscript is to propose a new distributed power allocation scheme for the downlink of distributed precoded multicell MISO-OFDM systems. By treating the multicell system as a superposition of single cell systems we define the average virtual bit error rate (BER) of one single-cell system, allowing us to compute the power allocation in a distributed manner at each BS. The precoders are designed in two phases: first the precoder vectors are computed in a distributed manner at each BS considering two criteria, distributed zero-forcing and virtual signal-to-interference noise ratio; then the system is optimized through distributed power allocation with per-BS power constraint. The proposed power allocation scheme minimizes the average virtual BER over all user terminals and the available subcarriers. Both the precoder vectors and the power allocation are computed by assuming that the BSs have only knowledge of local channel state information. The performance of the proposed scheme is compared against other power allocation schemes that have recently been proposed for precoded multicell systems based on LTE specifications. The results also show that although our power allocation scheme is based on the minimization of the virtual uncoded BER, it also has significant gains in coded systems.

Linear and Nonlinear Precoding Schemes for Centralized Multicell MIMO-OFDM Systems

The aim of this article is to propose and compare linear and nonlinear precoding schemes for multicell multiuser MIMO-OFDM based systems. The considered linear precoder is designed in two phases: first the intercell interference is removed by applying a linear zero-forcing algorithm. Then the system is further optimized by proposing three power allocation algorithms with per base station power constraint and different complexity tradeoffs: one optimal to minimize the average bit-error-rate and two suboptimal. The proposed nonlinear precoding is designed to minimize average bit-error-rate, over the users, conditioned to a channel realization and the transmitted data. In the high SNR regime, this problem simplifies from a constrained quadratic nonlinear optimization to a single quadratic problem with a scaling, allowing to reduce the complexity. The performance of the proposed schemes is evaluated, considering typical pedestrian scenarios based on LTE specifications. Numerical results show that the performance of the nonlinear scheme outperforms the linear ones. The nonlinear algorithm selects and inverts part of the correlation matrix unlike the linear zero-forcing where full inversion is required. This leads to a better performance as the selection allows to get a better conditioned matrix. Also, it is shown that the complexity of the nonlinear scheme is similar to the linear suboptimal closed-form one.

Limited Intersystem Information Exchange Method for Heterogeneous Networks

We consider a set of small cells deployed over a macrocell. To coexist under the same spectrum, the two systems must cooperate, so that one system can adapt to the other. In this letter, we assume that the macrocell has accurate channel information (about its channel and the cross-tier channels), which is quantized and then forwarded to the small cells. The aim is to minimize the required cross-tier information exchange rate and, at the same time, guarantee that the macrocell performance is not affected by the small cells. If the information exchange rate is adjusted as a function of the average SNR to achieve a BER target, then the required rate increases with the log of the average SNR. Therefore, we propose two adaptive approaches, where the exchange rate adapts to the instantaneous channel conditions, and show that the requirements can be exponentially reduced, i.e., only a log[log[.]] increase with the SNR is enough.