Author_Institution :
Dept. de Teor. de la Senal y Comun., Univ. Carlos III de Madrid, Leganés, Spain
Abstract :
In modern multiuser communication systems, users are allowed to enter or leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. The so-called problem of user identification, which consists of determining the number and identities of users transmitting in a communication system, is usually solved prior to, and hence independently of, that posed by the detection of the transmitted data. Since both problems are tightly connected, a joint solution is desirable. In this paper, we focus on direct-sequence (DS) code-division multiple-access (CDMA) systems and derive, within a Bayesian framework, different receivers that cope with an unknown and time-varying number of users while performing joint channel estimation and data detection. The main feature of these receivers, compared with other recently proposed schemes for user activity detection, is that they are natural extensions of existing maximum a posteriori (MAP) equalizers for multiple-input-multiple-output communication channels. We assess the validity of the proposed receivers, including their reliability in detecting the number and identities of active users, by way of computer simulations.
Keywords :
Bayes methods; MIMO communication; code division multiple access; radio receivers; wireless channels; Bayesian framework; DS-CDMA systems; MAP equalizers; activity tracking; computer simulations; data detection; direct sequence code division multiple-access; joint channel estimation; maximum a posteriori; multiple-input-multiple-output communication channels; multiuser communication systems; time-varying number; time-varying parameter; Channel estimation; Equations; Joints; Mathematical model; Multiaccess communication; Receivers; Vectors; Activity detection; code-division multiple access (CDMA); joint channel and data estimation; per-survivor processing (PSP); sequential Monte Carlo (SMC) methods;