Литература / UMTS-Report

A1.5.3 What is the base station antenna gain (dBi)?

The BS antenna gain is dependent on the environment where the BS operates, thus outdoor environments allow for higher antenna gain than indoor environments. Suitable antenna gains for the different environments are:

However, this information is independent from the air interface proposal.

A1.5.4 What is the mobile station antenna gain (dBi)?

The MS antenna gain is limited by the requirement to have a low cost, small sized handheld. Thus the antenna gain for low data rate mobile is expected to be 0 dBi. High data rate mobiles, maybe connected to a notebook, may allow for mobile antenna gain of 3 dBi.

A1.5.5 What is the cable, connector and combiner losses (dB)?

For the mobile the cable, connector and combiner losses can be neglected, thus they can be set to zero.

For the base station the cable, connector and combiner losses will be smaller compared to a GSM system due to the use of broadband carrier. The broadband carrier results in a large number of basic traffic channels and thereby in a high traffic value. Therefore, there is a much lower need for combining of several carriers as compared to GSM. Thus only a small number of carrier will be combined to one antenna reducing the overall losses to around 2 to 4 dB. However, this information is independent from the air interface proposal.

A1.5.5 What are the number of traffic channels per RF carrier?

The total number of traffic channels per RF carrier is 64 (each having a data rate of 16kbps for QPSK and 32kbps for 16QAM), whereas the carrier can be separated into 8 time slots in the TDMA domain and into 8 codes in the CDMA domain.

A1.5.6 What is the SRTT operating point (BER/FER) for the required Eb/N0 in the link budget template? Refer to section 2.4 of this part 5 of the evaluation report.

A1.5.7 What is the ratio of intra-sector interference to sum of intra-sector interference and inter-sector interference within a cell (dB)?

In case of sectorized cells, each sector will use different frequencies (see figure below), thus the intersector interference will be zero if only one cell is considered. Intra-sector interference is equal to zero in case of perfect channel estimation due to the joint detection process, that can't be achieved in reality, thus leading to small resulting interference after joint detection within one sector. Regarding one cell the above required ratio becomes 1.

Three sector cell (grey hatched) with hexagon sectors

A1.5.8 What is the ratio of in-cell interference to total interference (dB)?

The in-cell interference is completely suppressed in the joint detection process, thus the ratio of in-cell to total interference is theoretically in linear units equal to zero.

A1.5.9 What is the occupied bandwidth (99%) (Hz)?

The 99% bandwidth of a 1.6 MHz RF carrier is 2 MHz = 2 × 106 Hz.

A1.5.10 What is the information rate (dBHz)?

The information rate for one traffic channel per user including overhead (midamble, guard time), which is equivalent to allocate one code and one time slot of a carrier, is equal to 270.8 kbps equal to 54.32 dBHz for QPSK and 541.6kbps equal to 57.32 dBHz for 16QAM.

2 Detailed Explanation of Template

In this chapter detailed explanations are given for issues raised and adequately answered in the template.

2.1Rapid changing delay spread - A1.2.14.2

2.1.1 Varying tap coefficients vs. varying delay spread profile:

High vehicle speeds lead to high Doppler shifts. This is described by the fading of the individual model tap coefficients whose individual delays are fixed in case of a channel (real or simulation model) with many taps. The equalizer also uses an internal channel model. In case of many equally spaced taps in the internal equalizer channel model only the tap coefficients vary but the profile of the internal channel model of the equalizer remains constant, i.e. average tap amplitude and delay with respect to the channel model remain constant. In this case of many equidistant taps the time-variant channel coefficients can be tracked by adaptation algorithms like e.g. LMS or RLS during the bursts starting from the midamble.

2.1.2 Classification of speeds and required action:

The speed / Doppler shift can be classified into the following ranges:

Low: The channel coefficients do not vary considerably during a number of bursts or during the occurrence of several training sequences. In this case the channel estimation from the midamble can be averaged over several bursts for noise reduction. In case of frequency hopping only bursts with the same frequency are considered so that the speed for which this condition is met is much lower than for the case without frequency hopping - especially in case of random frequency hopping.

Medium: The channel coefficients do not vary considerably during one burst or from one training sequence to the next. In this case channel estimation from the midamble once per burst is sufficient to ensure proper detection.

High: The channel coefficients vary considerably during one burst or between one training sequence and the next. In this case adaptation algorithms are required in order to track the time-variant channel coefficients during the burst so as to ensure proper detection.

It is well-known from adaptation theory that adaptation causes noise amplification which worsens with faster adaptation. Just like slowing down the adaptation from once per burst by averaging over a number of bursts reduces the noise before detection, speeding up the adaptation increases the noise before detection. For the adaptation, algorithm noise also includes interference. For the same detection error rate the tolerable input noise (including interference) therefore increases for slower and decreases for faster adaptation. In order to

minimize this effect, additional noise generated internally by poor channel modeling and estimation errors caused by detection errors must be kept low. The following error propagation must be avoided: Detection errors cause wrong adaptation which in turn increases detection errors and so on so that the rest of the half-burst is lost.

The maximum permissible speed is chosen in the adaptation algorithms by a respective choice of the adaptation step size irrespective of the actual speed. This also selects the level of noise amplification and thus the level of reduction in S/N and C/I performance, i.e. of sensitivity and capacity. For GSM and a Viterbi equalizer the performance was observed to decrease only slightly with increased permissible speed up to a certain value, then moderately and finally extremely before instability occurred. The highest adaptation step size with only slight increase can be used as a standard value: Up to this permissible speed performance reduction becomes negligible. For higher speeds detection is still feasible although the required speeding up of the adaptation degrades performance in terms of sensitivity and capacity. The actual values depend on the equalizer type, equalizer implementation and the adaptation profile, however.

2.1.3Adaptation Procedure

Even without adaptation of the channel coefficients simulations have shown successful reception up to 500 km/h without noise. Obviously, a considerably reduced S/N or C/I performance results from the high speed. With any type of adaptation of channel coefficients during the burst noise enhancement by the adaptation introduces a worsening of C/I or S/N performance with faster adaptation.

For adaptation the half-bursts can be partitioned into small blocks during which the channel coefficients do not vary considerably. The detection uses the channel coefficients estimated during the previous block. The received signal is given by the convolution of codes, individual channels and data signals, followed by a summation over all users and addition of noise. Collecting the codes, the channels and the summation over all users in the overall channel matrix A, as well as all data signals in the data symbol vector d the received signal vector e is given by:

e = Ad + n

Detection according to e.g. the zero-forcing criterion can now be performed according to

^d = A+e

where + denotes the Moore-Penrose pseudo inverse (pinv in Matlab). Another expression for the received signal vector e is found by collecting the codes, the data symbols and the summation over all users in the spread signal matrix G, as well as all channel coefficients in the channel coefficient vector h:

e = Gh + n

After detection of a block the data symbols are known and the spread signal matrix G can be constructed. Then the channel can be estimated e.g. according to the zero-forcing criterion:

^h = G+e

This channel estimation is now used for the detection of the next block.

There is always a delay of one block between channel estimation and channel modeling during detection. This adversely affects the stability in case of very fast adaptation required for very high speeds. In order to minimize the delay the blocks need not succeed one another but can be interleaved: For example, the first block is assumed to extend from symbol # 1 to 10, the second block from symbol # 2 to 11, the third from symbol # 3 to 12 and so on. Thus a new estimation for each symbol period is available. In the example symbol 10 now belongs to 10 blocks: from block #1, where it is the last symbol, to block #10, where it is the first

additional GSM RX filter on top of the implementation of a single WB-TDMA/CDMA mode terminal. For bit rates within the WB-TDMA/CDMA mode of 144 kbps or higher a second receiver is necessary as well.

2.2.2 Spectrum co-existence

As UMTS provides much higher bit rates than GSM, it is expected that current GSM900/1800 operators might want to deploy UMTS technology to their existing frequency bands. In this case the services for UMTS and GSM might be offered from the same base station site.

Within this scenario, WB-TDMA/CDMA carriers can even be optionally adjacent to GSM900/1800 carriers. In this case the carrier spacing df between WB-TDMA/CDMA and GSM900/1800 links is 1.2 MHz so that the bit error rate does not become worse in any channel (please refer to Figure 2-1).

Figure 2-1 WB-TDMA/CDMA and GSM900/1800 carriers use adjacent channels

2.2.3 Handover between UMTS and GSM900/1800

The following item makes handover between UMTS and GSM and vice versa-feasible. For real time bearer services this handover will be efficient; for non real time bearer services which can be established in both systems this handover will be lossless.

∙WB-TDMA/CDMA provides a multiframe structure which is compatible to GSM. This makes a synchronization of a MS to both systems feasible.

∙The broadcast control channel (BCCH) and the random access channel (RACH) concept for WB-TDMA/CDMA uses a narrowband GSM-like 200 kHz channel -> backwards compatibility is achieved.

∙WB-TDMA/CDMA suggests as basic handover scheme to use mobile assisted and network evaluated handover similar to GSM.

∙The signaling scheme within the UMTS network concerning handover is compliant to GSM.

2.2.4 Other evolution aspects

∙WB-TDMA/CDMA allows the same frequency planning as GSM (except due to smaller cell sizes when used in a higher frequency band). Hierarchical cell layers are feasible. Thus, GSM base station sites can be reused for WB-TDMA/CDMA base stations.

∙The code planning within WB-TDMA/CDMA is not critical as there are plenty sets of codes available to be distributed among the adjacent cells.

∙As WB-TDMA/CDMA follows the same principles as GSM in many terms, the training of people, reuse of already existing capabilities, etc. is easy.

2.3Smart antennas - A1.3.6

WB-TDMA/CDMA does not require the use of smart antennas. But the resulting signal-to- interference-plus-noise-ratio (SINR) can be improved significantly by incorporating various smart antenna concepts on the uplink as well as the downlink. These SINR gains may be exploited

∙to increase the capacity (mainly in urban areas),

e.g., by reducing the cluster size, i.e., the number of cells per cluster, or by using 16 QAM instead of the QPSK data modulation,

∙to increase the coverage (mainly in rural areas),

e.g., by increasing the cell size (range extension) or by improving the edge coverage,

∙to increase the link quality,

∙to decrease the delay spread,

∙to reduce the transmission powers,

or a combination thereof. In the sequel, we present three different smart antenna concepts, namely

∙diversity antennas,

∙sector antennas,

∙and adaptive antenna arrays,

and show how these smart antenna concepts can be incorporated into the joint detection (JD) processes used in WB-TDMA/CDMA.

2.3.1 Diversity antennas

The first approach is the well-known space diversity concept using omnidirectional antennas with rather large distances between each other. The antennas are positioned at locations separated by several (e.g., 10) wavelengths l. Assume that K mobile users (their channels are separated by channel specific spreading codes) are simultaneously active on the same frequency and in the same time slot. On the uplink, a channel estimator estimates the channel impulse responses corresponding to the K connections between a particular diversity antenna and the mobile users according to the maximum likelihood channel estimation algorithm described in [8]. Based on the estimated channel impulse responses and the knowledge of the channel specific spreading codes, joint detection (JD) is performed using the JD algorithms

presented in [9]. On the downlink, the same signals are transmitted by all diversity antennas at the BS, and the receivers at the MS can remain unchanged. Moreover, diversity antennas may also be installed at the MS. Then the channel estimation and JD algorithms are executed as described before. Note that the use of diversity antennas at the MS may also be combined with the use of sector antennas or adaptive antenna arrays at the BS. This hold especially for MS for higher rate services e.g. lap tops.

Diversity antennas cause the radio channels between the BS and the corresponding mobiles to experience more or less independent fading processes. Due to this space diversity, the effective fading depths after combining are reduced. However, depending on the directional properties of the mobile radio channel, it is desirable to separate the antennas by rather large distances to guarantee independent fading processes. A similar effect may also be achieved by using polarization diversity. Notice that space diversity may also be combined with polarization diversity.

2.3.2 Sector antennas

In the second approach, the coverage area of a certain BS is served by a number of different fixed beams. These narrow beams with a predetermined direction can be implemented physically by a number of independent directive antennas (sector antennas) or virtually by adoption of an antenna array and an appropriate signal transform from elementto beamspace (e.g., by a Butler matrix). This signal transform corresponds to a fixed phase feed network (a beamformer) and provides several output ports corresponding to beams with fixed directions.

On the uplink, a channel estimator according to [8] is implemented for each sector antenna. This channel estimator determines the channel impulse responses for connections between the corresponding sector antenna and all users that are assigned to the considered BS. Based on the estimated channel impulse responses and the channel specific spreading sequences, the transmitted data symbols are estimated via JD as explained in [9]. On the downlink, each signal may be transmitted by only one sector antenna. For each mobile, the best sector antenna is determined from the corresponding channel impulse responses estimated on the uplink or another (uplink) performance criterion such as received-signal-strength or BER. This results in enhanced SINR values at the MS (mobile station) receiver. However, this assumes a mobile radio channel which is reciprocal in average.

The use of a set of non-adaptive directional antennas (sector antennas) results in a reduced time dispersion (reduced delay spread) of the observed transmission channels. Depending on the directions of arrival (DOAs) of the dominant wavefronts, a reduction of intercell and intracell interference can be achieved as well. Due to the fixed directivity of the sector antennas, no information about DOAs is necessary to implement the receiver. Nevertheless, if the number of sectors is sufficiently large, the directional inhomogeneity of the mobile radio channel can be exploited. Note that through the addition of a second set of sector antennas, the resulting system can also provide spatial or polarization diversity.

2.3.3 Adaptive antenna arrays

The most flexible and most powerful concept uses adaptive antenna arrays. If twodimensional array geometries like uniform rectangular arrays are used, one antenna array can cover the whole cell [10]. If a one-dimensional array geometry like a uniform linear array is employed, the cells must be sectorized and a single ULA only covers a sector of approximately -60° to 60° in azimuth.

First the user specific channel impulse responses are estimated for each antenna element independently as illustrated in Figure 2-2. In the depicted example, an antenna array with Ka = 4 antenna elements is used. Based on the Ka user specific channel impulse response vectors, the dominant directions of arrival (DOAs) are estimated for each user separately, cf. Figure

2-2. By using high-resolution direction finding schemes, we can estimate up to Ka -1 dominant DOAs per user [4].

Due to their simplicity and their high-resolution capability, ESPRIT-type algorithms are particularly attractive for this task. Here, the DOA estimates are obtained without nonlinear optimization and without the computation or search of any spectral measure [10]. Assume that all antenna elements have identical radiation characteristics (they might be omnidirectional or focused on a particular sector of interest). Furthermore, we use a centrosymmetric array configuration such that Unitary ESPRIT [10] can be used to estimate the dominant DOAs. Unitary ESPRIT retains the simplicity and the high-resolution capability of the original ESPRIT algorithm, but it is formulated in terms of real-valued computations throughout. This yields a substantial reduction of the computational complexity. Moreover, there is an efficient two-dimensional (2-D) extension for 2-D arrays with a dual invariance structure. This 2-D extension, known as 2-D Unitary ESPRIT, is a closed-form algorithm to provide automatically paired azimuth and elevation angle estimates [10].

Figure 2-2 Joint detection receiver structure for K simultaneously active users using an adaptive antenna array and direction of arrival (DOA)-based channel impulse response (CIR) estimation. In the depicted example, an antenna array with Ka = 4 antenna elements is used.

Using the estimated DOAs, improved DOA-based channel impulse response (CIR) estimates can be obtained for all users, cf. Figure 2-2. Thereby, the number of estimated channel parameters is reduced. The maximum likelihood DOA-based channel estimator and a simplified sub-optimum DOA-based channel estimator are described in [4]. Based on the dominant DOAs of all K users and the corresponding DOA-based channel impulse response vectors, the transmitted data is estimated via joint detection as illustrated in Figure 2-2. Here, the conventional channel impulse response estimates are simply replaced by improved estimates that take into account the dominant DOAs and the corresponding DOA-based channel impulse response vectors.

This DOA-based JD technique can be interpreted as a beamforming block followed by a signal separation part as explained in [5]. The beamformers maximize the signal-to-

The expression log2M is the number of bits per data symbol and Q.Tc/log2M is the bit duration at the output of the encoder. One net information bit is transmitted in a duration of Q.Tc/(Rc.log2M). Therefore, the equation (1-1) of the evaluation report part 3 is equivalent to C/I = (Eb/Tb)/(N0×B), i.e., C = Eb/Tb and I = N0×B with Tb the duration of a net information bit. The carrier to interference ratio per user is Kc times the carrier to interference ratio per CDMA code, with Kc denoting the number of CDMA codes per time slot per user.

Example: For speech the coding rate is Rc = 0.28 and equation (1-1) becomes:

In the following the link budget templates are calculated according UMTS 30.03. In addition some general information is given to assist in understanding the particular figures used in the template.

The average transmitter power per traffic channel is pre-set by UMTS 30.03. To achieve various data rates one or up to 8 timeslots and/or one or up to 9 codes are assigned to one particular user. Thus if one user is allocated 8 timeslots, the maximum transmitter power is equal to the average transmitter power. On the other hand if one user is allocated one timeslot, the maximum transmitter power is 8 times the average transmitter power.

Several questions are raised on the Eb/N0 and Rb assumptions, thus some exemplary calculations are performed for clarification:

Assuming QPSK (which is valid for nearly all test cases), the gross bit rate on the air is 270.8 kbps including midamble and guard period, 258.9 kbps including midamble and excluding the guard period and 235.7 kbps excluding midamble and guard period. Since no energy is needed to transmit the guard period, 258.9 kbps is used to determine the information bit rate in the template. All Eb/N0 values derived by the link level simulations are associated with the energy per bit needed to achieve the corresponding BER/FER. Thus the midamble is excluded in the Eb/N0 and therefore included in the information rate as explained above. The information bit rate is derived by multiplying 258.9 kbps with the total coding rate used by the service. That means the information rate becomes