**4.2 Existing digital bent-pipe satellite (DBPS) payload architecture**

**Figure 7** presents an existing DBPS payload architecture using on-board digital channelizer. Similar to analog payload, there are two options for the RF-to-IF downconversion process. Double-downconversion process is typically used for digital bent-pipe payload architecture.

**Figure 8** depicts typical RF-to-IF (or baseband) downconversion and digitization and sampling processes for a commercial DBPS payload architecture. The RF-to-IF process shown in this figure uses Option 1, double downconversion, and the digitization and sampling process employing bandpass sampling with

<sup>1</sup> Flicker noise is a type of electronic noise with a 1/frequency power spectral density.

#### **Figure 4.**

*Legacy ABPS payload architecture.*

#### **Figure 5.** *Options for RF downconversion and associated LO's phase noise.*

#### **Figure 6.**

*Functional block diagrams of MUX and DEMUX.*

digital quadrature technology [6]. The RF bandwidth (BW) associated with the RF bandpass filter (BPF) is selected to match with an over channel bandwidth (e.g., a maximum of 500 MHz for Ku-band). The automated gain control (AGC)

#### *Overview of Existing and Future Advanced Satellite Systems DOI: http://dx.doi.org/10.5772/intechopen.93227*

*Existing DBPS payload architecture.*

**Figure 8.**

*Typical R/F downconversion and digitization processing approach.*

is designed to maintain a constant power over the specified channel bandwidth. There are several advantages associated with bandpass sampling with digital quadrature techniques, including (a) no phase and amplitude imbalances; (b) digital finite impulse response (FIR) filters are flexible and computational complexity with linear phase introducing a constant group delay; (c) only one A/D converter is required (less weight and power); and (d) when the sampling period is set at one-quarter of the carrier frequency, the reference in-phase and quadrature components reduce to an alternating sequence between I-channel and Q-channel [6].

As shown in **Figure 9**, the key design issue associated with the digitization and sampling processing is the selection of required number of bits of the analogto-digital (A/D) conversion to (1) achieve optimum loading factor (LF) and (2) minimize the quantization noise. The LF is defined as the root mean square (RMS) of the total input signal voltage-to-A/D converter saturation voltage ratio. The total input signal voltage includes desired signal voltage (S) plus noise voltage (N) plus interference voltage (I). **Figure 10** illustrates an optimum LF as a function of number of bit of a typical A/D converter. As an example, for 4-bit,

#### **Figure 9.**

*Existing digitization and sampling processing using bandpass sampling with digital quadrature technique.*

**Figure 10.** *Optimum LF as a function of number of bit of A/D converter.*

the optimum LF is about 0.4. In conjunction with LF, the number of bit should be selected to maximize the signal-to-quantization noise ratio (SQNR) using the following relationship:<sup>2</sup>

$$\text{SQNR} \approx \mathbf{1.761} + \mathbf{6.02.N} \, dB \tag{1}$$

As an example, when *N* = 4 bits, signal-to-quantization noise ratio is about 25.84 dB.

The key feature of DBPS payloads is the flexibility of the digital channelizer. Current digital technologies allow for the implementation of robust and reconfigurable digital channelizer adapting to require the number of users and associated users' data rates. A typical flexible digital channelizer using polyphase/discrete Fourier transform (DFT) technology is shown in **Figure 11**.

As shown in **Figure 11**, the heart of a typical digital channelizer is a polyphasefilter network (or simply a polyphase network) and a DFT processor. A typical polyphase network with a DFT processor is described in **Figure 12**. The polyphase network consists of a set of NC digital filters with transfer function H0, H1..., HNc-1, which is obtained by shifting a basic low pass complex filter function along the frequency axis [7]. As an example, for a typical 500 MHz channel bandwidth, assuming for a typical user data rate of 4 MHz and a guardband of 1 MHz, digital channelizer, NC = 500/(4 + 1) = 100, that is, the number of filter is 100, and each has a total of 5 MHz bandwidth. A change in sampling frequency by a factor of NC can

<sup>2</sup> Quantization (signal processing). Available from: https://en.wikipedia.org/wiki/ Quantization\_(signal\_processing).

*Overview of Existing and Future Advanced Satellite Systems DOI: http://dx.doi.org/10.5772/intechopen.93227*

**Figure 11.**

*Typical digital channelizer using polyphase/DFT technology.*

**Figure 12.** *Typical Polyphase/DFT Technology.*

be introduced, thus allowing the circuit in different paths of the polyphase network to operate at lower frequency than the original sampling frequency. A practical implementation of a high-throughput low-latency polyphase channelizer can be found in [8, 9].

**Figure 12** shows an example of five input signals, namely S1, S2, S3, S4, and S5, and the channelizer will select signal interest by filtering out the other signals. As an example, the signal line with the filter transfer function of H0 filters out S2, S3, S4, and S5 and sends S1 as an output signal.

### **4.3 Advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB) payload architecture**

For a typical commercial HTS system architecture, it usually requires on-board multiple beam phase array (PA) antenna with associated adaptive digital beamformer network (DBF) for spot beamforming and frequency reusing of the spot beams when the beams are not located near each other. **Figure 13** describes a typical AdDBPS-DCB payload architecture, where the digital channelizer is combined with a DBF to make a "digital channelizer and beamformer" (DCB) [10–12]. For this payload architecture, the key feature that differentiates this architecture with the ones discussed above is the combined digital channelizer using polyphase network/ DFT processor and DBF (PolyN/DFT-DBF).

As pointed out in [10–12], DCB architecture shown in **Figure 13** can be designed to (1) form individual beams for each active receive and transmit communication channels; (2) adaptively generate channel beam steering weights to dynamically vary the bandwidth, location, and shape of each beam based on traffic demands and the locations of other, potentially interfering beams avoiding adjacent channel interference; (3) use digital beamforming weight calibration to compensate for the temporal and thermal phase and amplitude response variations inherent in analog multibeam phased array antennas; and (4) adjust the gain of individual

receive-and-transmit channel beams automatically to compensate for propagation path and analog payload response variations. In general, there are two possible DCB implementation approaches, namely DCB Approach 1 and DCB Approach 2 [13]. **Figure 14** describes the DCB Approach 1 for processing the uplink signals, where the uplink signals are individually processed by the digital channelizer (i.e., PolyN/ DFT processing) and DBF independently and separately. DCB Approach 1 requires a larger computational load because each DBF processes all the user link bandwidth (e.g., S1, S2, S3, S4, and S5 in **Figure 12**) at all times to form multiple beams.

DCB Approach 2 is shown in **Figure 15**, where DCB utilizes an unified processing approach with each DBF processes only the bandwidth corresponding to a beam (S1 in **Figure 12**) at normal times. During anomaly operation condition (e.g., natural disaster event), when the bandwidth has to be reassigned to specific areas, the arithmetic load on DBF can be reduced by implementing multiple DBFs, with each capable of processing a bandwidth narrower than that assigned to a beam (i.e., smaller channel unit). This approach enables a reduction in wasteful arithmetic resource usage on bandwidth.

If one defines the number of multipliers, D implemented in each Tx/Rx DBF as C/fop, where C is the computational load of a DBF (multiplications/sec), and fop is the operation frequency of the multiplier. Let us compare D calculations between DCB Approach 1 and DCB Approach 2. Let us assume the following parameters: *n* is the number of array elements, *m* is the number of beams, an userlink processing bandwidth of 28 MHz, 5 frequency repetitions of the userlink, and an operating

**Figure 13.** *AdDBPS-DCB payload architecture.*

**Figure 14.** *DCB Approach 1: PolyN/DFT and DBFN individual processing.*

**Figure 15.** *DCB Approach 2: Unified and combined PolyN/DFT and DBFN individual processing.*

frequency of multiplier of 256 MHz. Using these values, D for the DBF/channelizer of the DCB Approach 1 configuration becomes [13]:

$$\frac{\left(n \times 4 \times m \times 28 \ast 10\mathbf{6}\left[\text{multiplication}\,\text{/s}\right]\right)}{\left(25\mathbf{6} \ast 10\mathbf{6}\left[\text{multiplication}\,\text{/s}\right]\right)}\tag{2}$$

and that for DCB Approach 2 configuration becomes [13]:

$$\frac{\left(n \times 4 \times m \times 28 \ast 106 \text{ /5} \left[\text{multiplication}/\text{s}\right]\right)}{\left(256 \ast 106 \left[\frac{\text{multiplication}}{\text{s}}\right]\right) \times 2} \tag{3}$$

The latter calculation assumes an ideal case in which DBF network (DFBN) processing is performed on a channel-by-channel basis. The complexity of DCB Approach 2 configuration is 10 times less complex than DCB Approach 1.

As pointed out in [12], the DBFN when coupled with a digital channelizer (aka DCB) offered more capabilities with many advantages. Nguyen et al. [14] developed a computer simulation model of a typical DBFN in MATLAB and presented simulation results for X, Ku, and Ka BFNs using 60-element, 104-element, and 149-element, respectively. **Figure 16** is an extracted Ka-band BFN result showing the achievable antenna gain of 45.5 dB at 3-dB beamwidth of 0.9°. For practical applications, the DBFN will shape the beam size depending on the coverage area and desired number of beams. Nguyen et al. [14] pointed out that for 2.5° coverage area and the desired number of beams of 7, the minimum 3-dB beamwidth of 1.1° is required. Nguyen et al. [14] also pointed out that DCB can provide a significant increase in frequency reuse, where the frequency reuse is defined as the number of times a satellite can reuse the same spectrum and frequencies. High frequency reuse factor can cause potential cochannel interference (CCI) that results in a decrease in carrier-to-interference power ratio [aka (C/I) CCI]. As pointed out in [14], for dynamic allocation using real-time allocation of beams so that the coverage radius of a cell is equal to the satellite pointing error, assuming satellite pointing error of 0.02 degree pointing error, the (C/I)CCI is about 25 dB for frequency reuse factor 40 [14].
