Performance Evaluation of Open Loop Multi-User MIMO Systems Naga Sekhar Suruvu 1† , Sivakishore Reddy Yerrapareddy 1 , Kiran Kuchi 1 , Giridhar Krishnamurthy 2 1 CEWiT, IITM Research Park, III rd floor, Taramani, Kanagam Road, Chennai-600113, India. 2 Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai-600036, India. Emails: {shekhar, kishore, kkuchi}@cewit.org.in, giri@tenet.res.in Abstract—Significant throughput gains can be achieved in multi-user MIMO (MU-MIMO) wireless system by exploiting the combination of multi-user scheduling and multi-user diversity. Open loop MU-MIMO (OL-MU-MIMO) is a codebook based precoding technique where precoders are fixed a priori at the base station (BS) in a known fashion and the user needs to feedback which precoding vector is to be chosen referred to as preferred vector index (PVI) or stream indicator. This scheme feedbacks the channel quality information (CQI) which is used by the BS for allocation of modulation and coding scheme (MCS) to the scheduled users. As the precoders used at all the base stations are known a priori, estimation of co-channel interference (CCI) is accurate and there is negligible mismatch between the CQI fed back by the user and SINR experienced by the user during next frame for low Doppler, resulting in stable CQI modeling. In this paper, an extensive study is made on OL-MU-MIMO and open loop single-user MIMO (OL-SU-MIMO), with an emphasis on how OL-MU-MIMO exploits multi-user diversity to achieve high spectral efficiencies. We also derive the SINR and CQI expressions for such MU-MIMO systems, and provide simulation results which indicate that OL-MU-MIMO outperforms OL-SU- MIMO only when there are large number of users in the system. Index Terms—MU-MIMO, Open loop MU-MIMO, Multi-user diversity, Codebook based precoding, CQI, IEEE 802.16m. I. I NTRODUCTION Multi-user MIMO is a technique, where the BS schedules multiple users to use the same time-frequency resources. In MU-MIMO, the additional spatial degrees of freedom are shared between multiple users, and individual user throughputs increase due to the fact that users gets scheduled more often to reuse the same time-frequency resources, without consuming extra bandwidth or power. Hence, unlike traditional schemes that rely on good channel conditions for separation of data streams, MU-MIMO system exploits the multi-user diversity [3] and schedules a set of users such that each user causes minimum amount of interference to the remaining set of users. MU-MIMO precoding schemes will offer tremendous ad- vantages if channel state information (CSI) is available at the transmitter. But it is huge amount of feedback to send complete channel. There are many limited feedback systems proposed in the literature [4]-[6]. Codebook based precoding techniques are also proposed to reduce the amount of feedback involved. The codebook consists of a set of precoding matrices each comprising of one or more precoding vectors depending on the † This work was done by Naga Sekhar Suruvu when he was with Depart- ment of Electrical Engineering, IIT Madras, Chennai-600036, India. number of streams allocated to the user. These codebooks are pre-designed theoretically based on sounding criterion [7][8] and is known both at the transmitter and receiver. In a linear precoding system, the transmitted data vector is pre-multiplied by a precoding matrix or precoder for simple. Based on the feedback mechanism involved and construc- tion of precoders at the BS, MU-MIMO system can be classified into 1) Open Loop MU-MIMO and 2) Closed Loop MU-MIMO. OL-MU-MIMO is a codebook based precoding technique in which precoders are fixed a priori at all the BSs and is known to all the users in the system. Precoder is formed by choosing a set of unitary precoding vectors from the codebook. In a closed loop system, the precoders are not fixed and they are formed based on the feedback from users. In this paper, we focus on OL-MU-MIMO and OL-SU- MIMO as prescribed in the IEEE 802.16m WMAN standard [1][2]. In OL-MU-MIMO, each user feedbacks a) PVI and b) CQI for every subband which is used by the scheduler in the subsequent frame. PVI is used by the BS to decide which of the precoding vectors is to be used for the user to precode his data. CQI is used for link adaptation where the BS varies the MCS allocated to a user to suit his channel conditions. In OL-SU-MIMO, only one user with single stream is scheduled per resource block where single precoding vector is used at the BS and hence, each user feedbacks only CQI. CQI is an estimate of the SINR a user is likely to experience in the next frame. Using channel estimates made through dedicated pilots, a user can estimate his CCI levels in the current frame. Since all the BSs are using same set of precoders which are fixed a priori, interference from the neighboring BSs to a particular user can be estimated accurately even when the precoder is not unitary. This is the major advantage of open loop system when compared to the closed loop system where the accurate CCI estimation is impossible when the precoder is not unitary. Hence the CQI modeling is more stable and reliable in case of open loop system resulting in optimal MCS assignment. This paper is organized as follows: Section II introduces the OL-MU-MIMO system, signal model and 802.16m frame structure. Section III describes the MMSE receiver and SINR calculations. Section IV presents CQI and PVI computations, feedback mechanism and proportional fair (PF) scheduling algorithm. In Section V OL-MU-MIMO operation is compared with OL-SU-MIMO, with an emphasis on what happens when there are large number of users per sector. Section VI presents the simulation results and Section VII concludes the paper.