relay nodes are introduced within the cell, the BS and the multiple RNs will interfere each other. This kind of interference, referred to as inter-sub-cell interference, are fully modeled in the investigation on resource allocation among BS and RNs in the same REC in Chapter 5.
• Equal power distribution over the whole bandwidth
The total transmit power is usually limited by the capability of the hardware, as stated in Table 2.1. It is further assumed that the total transmit power is equally dis-tributed over the available bandwidth, which simplifies the resource allocation prob-lem. Moreover, the constant power allocation on all sub-carriers allows to predict the inter-cell interference, which is desired for inter-cell interference management.
Nevertheless, the power allocation among the chunk layers is still enabled.
• Infinitely backlogged user queues
The user queues are assumed to be infinitely backlogged. This means that, when one user is scheduled for transmission, it always has some data packets to transmit.
• Mutual Information based Link to System Level Interface
The simulation results presented in this work are obtained through system level sim-ulations. The performance is evaluated in terms of data throughput, defined as the number of information bits correctly received at the receiver, i.e.
ρ=ρtx·(1−CWER), (2.52) where ρtx denotes the number of information bits transmitted. The CWER is de-rived according to the link quality using a mutual information based performance model [BKA05]. A brief description of the approach is provided as follows.
1. Calculate the receive SINR values for all resource elements used by the FEC code word of interest. The receive SINR depends on power allocation, trans-mit and receive processing as well as instantaneous channel and interference characteristics.
2. Compute average mutual information per bit (MIB) according to
MIB = PQ
q=1IMq
SINRq
β
PQ
q=1mq
, (2.53)
where
2.8 Further Assumptions
SINRq is the SINR on resource elementq,
Mq is the identifier of the modulation format applied on resource elementq,
mq is the number of bits represented by the symbol transmitted on resource elementq,
IMp is the mutual information associated with the modulation format Mp as a function of SNR/SINR [CTB96],
β is the optimization parameter to be derived from link level simulations, which is chosen to be 1 in this work [BKA05].
3. Map the average MIB in (2.53) to a CWER. The mapping between MIB and CWER is generally specific to code rate, code type and code word length [BKA05] and is derived from link level simulations.
3 Adaptive Resource Allocation in a Single Cell
3.1 Introduction
As stated in Section 2.4, OFDM is a low complexity technique to bandwidth efficiently modulate parallel data streams to multiple carriers. It is considered as the leading tech-nique for the next generation wireless communication systems [STT+02]. An OFDM sys-tem can support the simultaneous transmission to multiple users with different and variable data rate by assigning them a different number of disjoint sub-carriers in a FDMA fashion, referred to as OFDMA. This provides high flexibility and granularity. Since the channel fading is frequency-selective and independent among different users, the system perfor-mance can be improved by means of adaptive sub-carrier assignment as well as bit and power loading [WCLM99, RC00], referred to as adaptive OFDMA hereafter.
Moreover, when the AP, either BS or RN, is equipped with multiple antennas, the spec-trum efficiency can be increased by SDMA, i.e. by spatially separating multiple users served on the same time-frequency resource by means of orthogonal or semi-orthogonal antenna beamforming [FN94]. The number of users separable by SDMA is generally lim-ited by the number of multiple antenna elements and the achievable capacity depends on the spatial correlation among users. Therefore, the system performance can be optimized by properly selecting the groups of users sharing the time-frequency resources [DS05], whereas is referred to as adaptive SDMA in the following.
Adaptive OFDMA in wide-band OFDM systems has been well studied in [WCLM99, RC00] and many algorithms for adaptive SDMA in narrow-band flat-fading channel are also proposed in the literature [FGH05, JG04, DS05]. However, adaptive resource al-location becomes more challenging in a frequency-selective broadband OFDM multiple antenna system, where both adaptive OFDMA and adaptive SDMA are desired.
In [ZL05], it is assumed that the users’ performance will not be affected by sharing resources with users when their spatial correlation is sufficiently low over the whole trans-mission bandwidth. Thus, the adaptive resource allocation is proposed to be separated into two steps: spatially correlated users are firstly grouped together while ensuring low spatial correlation between users in different groups, and then adaptive OFDMA is independently
carried out within each group. However, it is observed that the user spatial correlation properties are frequency-selective in some cases such as the considered urban-macro sce-nario, which causes troubles in the application of this two-step approach. Intuitively, the frequency-selective user spatial correlation over the whole bandwidth could be measured by the average, but the approach still will not provide good results compared to joint opti-mization of adaptive OFDMA and adaptive SDMA.
In [Wil06] spatial compatible users are adaptively selected to build an SDMA user group on each time-frequency resource, i.e. chunk, but no dependency exists among allocation of each chunk. The ordering of the chunks is non-adaptive to the channel conditions, which is referred to as disjoint OFDMA/SDMA hereafter.
Different to the these approaches, joint optimization of adaptive OFDMA and SDMA is introduced by the author of this thesis in [CZT07] and will be discussed in this chapter. The joint approach intends to make joint optimization of the adaptive resource in both frequency and spatial domains, and it is expected to provide better results. As the computational complexity of the optimal solutions is unaffordable in practical systems, cf. Section 3.2.3, alternative sub-optimal algorithms are proposed.
In this chapter, adaptive resource allocation for the DL transmission at one AP in a single cell is studied. This is supposed to be performed at the AP for each frame. Perfect full CSI is available at the transmitter, i.e. the AP, and the channel fading is flat within one chunk.
The AP is equipped with multiple antennas and users may share the same chunk by means of ZFBF, cf. Section 2.5. All users are assumed to be equipped with single antenna, but the proposed algorithms are applicable in the case that users have multiple antennas as well, since a user equipped withMrk antennas can be viewed as Mrk single-antenna users, cf.
Section 2.5.
According to the optimization objective function, two classes of optimization problem in the literature have been identified for adaptive resource allocation , power minimization problem [WCLM99] and rate maximization problem [RC00]. The power minimization problem aims at achieving the minimum total transmit power under the constraint on the users’ data rate. The rate maximization problem is intent on maximizing the sum of the user’s data rate under the constraint on the total transmit power. Correspondingly, the power minimization problem and the rate maximization problem are solved in Section 3.2 and Section 3.3, respectively.
In solving the power minimization problem, several variants of the greedy sub-optimal algorithm, as summarized in Table 3.1, are proposed and compared. Moreover, the optimal solution achieved by exhaustive search is also presented so as to evaluate the performance degradation of the sub-optimal solutions with respect to the optimum.
In solving the rate maximization problem, the user fairness metric is a very important factor [KMT98]. Indeed, rate maximization favors user with good channel quality and thus resulting unfairness among users with different channel quality, known as near-far