**4. MU-MIMO vs SU-MIMO**

6 Recent Trends in Multiuser MIMO Communications

is then:

• *a*<sup>+</sup> = max(*a*, 0)

*<sup>b</sup>* is the noise signal power

allows for a significant improvement of the BER.

**3. Multi-user MIMO system**

• *σ*<sup>2</sup>

systems.

*R* = *min*(*NR*, *NT*) is the rank of the channel matrix *H*

*CWF*(*H*) =

• *<sup>λ</sup>H*,*<sup>p</sup>* is the *<sup>p</sup>* − *th* singular value of the channel matrix *<sup>H</sup>*

• *µ* is a constant scalar which satisfies the total power constraint

*R* ∑ *p*=1 log2 

3. When CSI is available at both the transmitter and the receiver, the channel capacity is computed by performing the water-filling algorithm. The instantaneous channel capacity

We consider the case where CSI is available at the receiver, the simulated ergodic MIMO capacity is depicted in Figure 3. For a MIMO system with two transmit antennas, numerical results show that ergodic capacity linearly increases with the number of antennas. Plotted curves are presented for different levels of the SNR. The use of additional antennas improves the performances of the communication system. Moreover, MIMO system takes advantage of multipath propagation. The performances of MIMO system are observed in the following in terms of the Bit Error Rate (BER). We consider a MIMO system with various receive antennas, the BER is evaluated for communication systems with Rayleigh fading MIMO channel and additive gaussian noise. At the receive side, the Maximum Ratio Combining (MRC) technique is performed. According to Figure 4, it is obvious that MIMO technology

Once the MIMO technology is presented, we introduce in the following multi-user MIMO

The growth in MIMO technology has led to the emergence of new communication systems. We are particularly interested in this chapter in multi-user MIMO (MU-MIMO) ones [11]. MU-MIMO [12] system is often considered in literature as an extension of Space-Division Multiple Access (SDMA). This technology supports multiple connections on a single conventional channel where different users are identified by spatial signatures. SDMA uses spatial multiplexing and enables for higher data rate. This could be achieved by using multiple paths as different channels for carrying data. Another benefit of using the SDMA technique in cellular networks is to mitigate the effect of interference coming from adjacent cells. Traditional communication MIMO systems are usually referred as single-user MIMO systems (SU-MIMOs) or also point-to-point MIMO. Case of MIMO systems, the access point communicates with only one mobile terminal (the user). Both the access point and the mobile terminal are equipped with multiple antennas. In contrast to the single-user case, the access point is able to communicate with several mobile terminals. SU-MIMO and MU-MIMO systems are two possible configurations for multi-user communication systems. We also find other configurations in literature such as MU-MIMO with cooperation where cooperation is

�*λH*,*<sup>p</sup>* · *<sup>µ</sup> σ*2 *b*

�+

*bits*/*s*/*Hz* (6)

Table 1 summarizes the main features of both SU-MIMO and MU-MIMO systems [13]. In contrast to MU-MIMO systems where one base station could communicate with multiple users, base station only communicate with a single user in the case of SU-MIMO systems. In addition, MU-MIMO systems are intended to employ multiple receivers so that to improve the rate of communication while keeping the same level of reliability. These systems are able to achieve the overall multiplexing gain obtained as the minimum value between the number of antennas at base stations and the number of antennas at users. The fact that multiple users could simultaneously communicate over the same spectrum improves the system performance. Nevertheless, MU-MIMO networks are exposed to strong co-channel interference which is not the case for SU-MIMO ones. In order to solve the problem of interference in MU-MIMO systems, several approaches have been proposed for interference management [14][15]. Some of these approaches are based on beamforming technique [31]. Moreover, in contrast to SU-MIMO systems, MU-MIMO systems require perfect CSI in

11

http://dx.doi.org/10.5772/57133

*Rx*<sup>1</sup>

*U*<sup>1</sup>

✿✸

③✿

*RxM*<sup>1</sup>

. . .

*Rx*<sup>1</sup>

*UK*

❘

�

✇

⑦

(b) Downlink MU-MIMO

Multi User MIMO Communication: Basic Aspects, Benefits and Challenges

∗} <sup>=</sup> *IMk* ; *<sup>k</sup>* <sup>=</sup> 1, . . . , *<sup>K</sup>* (7)

*Hk* · *Xk* + *<sup>b</sup>* (8)

. . .

*RxMK*

*<sup>b</sup>* . The uplink

. . .

users transmit signals to the base station. However, in the case of downlink communication, base station transmits signals to users. A representation of these systems is depicted in Figure 6. We assume that the base station is equipped with *N* antennas. Case of DL-MU-MIMO, the base station attempts to transmit signals to *K* users *U*1,. . . ,*UK* which are respectively

For notations, if antenna *k* acts like a receiving antenna, it is denoted by *Rxk*. Otherwise, it

*Tx*<sup>1</sup>

BS

*TxN*

. . .

*Tx*<sup>1</sup>

*U*<sup>1</sup>

*TxM*<sup>1</sup>

. . .

*Tx*<sup>1</sup>

*UK*

. . .

*TxMK*

Let *Xk*(*Mk* × <sup>1</sup>), the transmit signal vector of user *Uk*; *<sup>k</sup>* = 1, . . . , *<sup>K</sup>*. We assume that data streams associated to user *Uk*; *k* = 1, . . . , *K* are zero mean white random vectors where :

The complex channel matrix relating user *Uk*; *k* = 1, . . . , *K* to the base station, *Hk* is of dimension (*<sup>N</sup>* × *Mk*). In presence of additive noise signal *<sup>b</sup>*(*<sup>N</sup>* × <sup>1</sup>), the received signal

• It should be as many receive antennas at the base station as the total number of users

. . .

equipped with antennas of numbers *M*1,..., *MK*.

(a) Uplink MU-MIMO

E denotes the expected value operator.

scenario should satisfy two constraints:

antennas.

**Figure 6.** MU-MIMO communication models: UL-MU-MIMO and DL-MU-MIMO

<sup>E</sup>{*XkXk*

vector at the base station, *y*(*N* × 1) is expressed in the slow fading model by:

*y* = *K* ∑ *k*=1

The noise signal vector is a zero mean white Gaussian variable with variance *σ*<sup>2</sup>

is denoted by *Txk*.

BS

*Rx*<sup>1</sup>

✾

②

■♦✻

*RxN*

**5.1. UL-MU-MIMO**

✾

⑥✐

✰

. . .

**Figure 5.** MU-MIMO configurations

order to achieve high throughput and to improve the multiplexing gain [16]. Finally, the performances of MU-MIMO and SU-MIMO systems in terms of throughput depend on the SNR level. In fact, at low SNRs, SU-MIMO performs better. However, at high SNRs level, MU-MIMO provides better performances.


**Table 1.** Comparison between SU-MIMO and MU-MIMO systems
