Sequans

About: Sequans is a company organization based out in Reading, United Kingdom. It is known for research contribution in the topics: Communication channel & MIMO. The organization has 73 authors who have published 126 publications receiving 1066 citations.

Method of detecting interference in a communication signal

Abstract: A method of detecting at least a radar signal in an incoming signal received by a wireless communication system, comprising the steps of obtaining a measurement of said incoming signal during a measurement duration proportional to a known pulse duration of a known radar signal, performing a first comparison between said measurement and a first threshold, performing an intermediate test if said measurement exceeds said first threshold, characterized in that it further includes the step of performing a second comparison between said measurement and a second threshold if said measurement exceeds said first threshold, said second threshold being greater than said first threshold, and in that said intermediate test is only conditionally performed if said measurement exceeds said second threshold.

Full Frequency Reuse in OFDMA-Based Wireless Networks with Sectored Cells

Abstract: Due to overlap of antenna radiation patterns and the resulting interference between sectors, different frequency bands are typically used in different sectors of a cell in cellular systems. In this paper, a full frequency reuse concept, which is based on multiple-input multiple-output (MIMO) techniques, is introduced and analyzed for the uplink and downlink. The results indicate a significant increase of the cell capacity.

Method for transmitting and estimating symbols coded with a coding matrix, and corresponding receiver and transmitter

Abstract: Method for transmitting a sequence of symbols through at least a channel in a wireless communication system, comprising at least the steps of : forming a coding matrix (MC) with said sequence of symbols, each component of the coding matrix (MC) being a linear combination of two symbols among a first, a second, a third and a fourth symbols (S1, S2, S3, S4), transmitting a first and a second components of a first column and of a second column of the coding matrix through a first and a second transmit antennas (Tx1, Tx2), at a first and a second time slot (T1, T2). The coding matrix (MC) is defined as in Formula (I) wherein a, b, c, d being complex numbers of modulus 1.

Spectrum Aggregation: Initial outcomes from SAMURAI project

Abstract: Multi-user MIMO and Spectrum Aggregation (also referred to Carrier Aggregation) are two key enablers of next generation wireless systems. Although those techniques were already quite well investigated at theoretical level, their practical implementation is not immediate and raises numerous challenges. The SAMURAI project aims at investigating such challenges as well as at providing realistic performance results. This paper presents first outcomes of the project, with a specific focus of Carrier Aggregation (CA). Two aspects are discussed: CA at PHY/RF level with implementation challenges. Then, the system aspect of CA, providing new flexibility in terms of radio resource management is investigated. System simulations show capacity improvement when CA is enabled. SAMURAI outcomes in the MU-MIMO aspects are reported in a companion paper [9].

Method of decoding a spatially multiplexed signal and its corresponding receiver

Abstract: An embodiment of a method for decoding a received signal function of at least a channel matrix B, and of a first and second symbols S1, and S2 belonging to a signal constellation. The method comprises the steps of: selecting a set of values of the first symbol S1 in the signal constellation; for each selected value of the first symbol S1: estimating the value of the second symbol S2 to generate an estimated value of the second symbol; calculating an Euclidean distance between the received signal and a noiseless signal defined by the first symbol with said selected value and by the second symbol with said estimated value; selecting the minimal Euclidean distance among the Euclidean distances respectively calculated for the different selected values of the set of possible values of the first symbol; and selecting decoded first and second symbols corresponding to the selected minimum Euclidean distance.