Notation α Path Loss Exponent ηEE Energy Efficiency ηSE Spectral Efficiency C Channel Capacity c Speed of light in air Complex n dimensional vector diag Diagonal Matrix E Expectation Operator Eb Energy per Information bit Element of, set membership IM Identity Matrix ∞ Infinity ln(.) Natural Logarithm N0 Noise Power Spectral Density Nr Number of receive antennas Nt Number of transmit antennas P Power Pt Transmit Power Pc Circuit Power Po Output Power Ptot Total Power ρ Amplifier Efficiency Efficiency W Watt
experienced in a dense small cell deployment. Significantly, the work shows that despite LSAS and massive MIMO schemes being dominant themes in current research, the use of single omni-directional SBSs with suitable forward error correction (FEC) provides a low complexity and energy efficient alternative for the indoor SBS. The work discusses some possible drawbacks associated with massive MIMO for 5G SBSs. It states the case for alternative architectures. An air interface based on direct sequence spread spectrum is proposed for 5G indoor small cells. Using spatial and temporal processing, through beamforming and diversity combining of a DS-SS based physical layer, the predicted performance is presented. A detailed analysis of circuit power due to signal processing is provided. The new air interface is compared to a 4G OFDM based system and is shown to be more power efficient and significantly the SE/EE trade-off is improved by an order of magnitude i.e. a 10-fold improvement is seen. List of Publications and Presentations The research carried out during the course of this PhD has resulted in the following publications: Journal Articles Muirhead, D.; Imran, M.A.; Arshad, K., "Insights and Approaches for Low-Complexity 5G Small-Cell Base-Station Design for Indoor Dense Networks," in Access, IEEE, vol.3, no., pp.1562-1572, 2015. D. Muirhead; M. Imran; K. Arshad, "A Survey on the Challenges, Opportunities and Use of Multiple Antennas in Current and Future 5G Small Cell Base Stations," in IEEE Access , vol.PP, no.99, pp.1-1, 2016.
(2.19) per degrees of freedom (DOF), where is the noise power spectral density. According to the Nyquist sampling theory, DOF is 2W. Therefore, the channel capacity is C=2WR bits per second. The channel capacity in bits/s is given by [101]: (2.20) Consequently SE is given by: (2.21) Consequently EE is: (2.22) As a result, the SE-EE relation can be expressed as: (2.23) (2.24) From (2.24), it can be shown that decreases monotonically with R. With max =1/(ln2) as R→0, and min = 0 as R →∞ [101]. The results obtained from (2.24) assume an infinite size of information block and infinite number of DOF. In the finite case the behaviour will be different. In practice the EE bounds from the information-theoretic analysis might not be achieved in practical systems due to performance loss of capacity-approaching channel codes e.g. Turbo codes, imperfect knowledge of the CSI, the performance of the synchronization subsystem and transmission associated electronic circuit consumption [101]. When considering the circuit power EE needs to be re-defined as information bits per unit energy i.e. not just the transmitted energy, where additional circuit power, Pc, needs to be included: (2.25) The EE-SE trade-off is shown in Figure 2.10. In particular, circuit power will break the monotonic relation between SE and EE. If circuit power is considered it will turn the curve into a bell shape. Figure 2.10 EE/SE trade-off in AWGN
Alamouti for Frequency Selective Channels The Alamouti scheme can be extended to consider wideband, frequency selective fading channels such as those applicable to indoor small cells. The authors in [104] discuss receiver techniques to cope with wideband channels based on the W-CDMA physical layer. Performance of these techniques is explored in the following, for the case of the small cell. Figure 3.3 Multipath STTD Consider Figure 3.3. The received signal on path j by the transmitted signal at time instant T is given by: (3.4) where denotes the Rayleigh fading of the jth multipath of the ith transmit antenna at the receiver. Similarly at time : (3.5) For each jth multipath, the UE does the following linear processing to generate soft symbols respectively: (3.6) Soft outputs from all multipaths can be combined to generate the net output for symbol as: (3.7) similarly for (3.8) Simulation of Alamouti for W-CDMA
whereis the power consumed considering the processing (RF and Baseband) requirements of the solution. When considering the losses due to the supply and conversion of electricity to the system, the total power can be determined as (3.19), where DC-DC conversion loss and power supply loss are given as proportional to the respective sub-systems they feed as per Figure 3.14. (3.20) Table 3.7 Parameters Used Parameter Description Number of power amplifiers Transmit Power for single SISO scheme (W) PA efficiency RF transceiver power – single radio (W) Power supply loss (%) (including DC-DC loss) Signal processing power – single radio (W) Transmit power reduction due to antenna scheme DC-DC loss conversion loss (%) Power supply loss (%) Total power for antenna power reduction scheme (W) For the case where additional antennas are used for transmit power reductions in the active state, the level of reduction should be included in the analysis i.e. the system is not concerned with spectral efficiency (SE) or capacity but it is used to maintain a quality of service with a lower overall power than would be possible with a single antenna. This shows the relationship between power reduction and number of antennas for any given number of antennas. It is assumed that the baseband signal processing requirements are low and would have minimal effect on consumed power. The main power would be attributed to the additional RF transceivers and PAs. However, this is confined to this particular implementation of W-CDMA. LTE and Transmit Diversity
It is assumed that a maximum of 4 users can be served by the SBS when the SF=4 or 8 users when the SF=8. Each user, k, is assigned a pseudo noise (PN) sequence of Nc chips defined as: (6.2) is the nth chip where elements of are +/- 1. Assuming the system is sampled at the chip rate, transmission is represented as: (6.3) where is the ith information bearing signal, lasting for chips. The signal in the presence of noise is received as: (6.4) where =user u’s channel response. is the integer delay of user u and is additive noise. Coherence Time The coherence time ( tells us tells us the time that the channel remains constant due to the effects of Doppler. Doppler frequency impacts at mmWave frequencies were shown as an important consideration to make (section 5.2.3). Assuming carrier frequencies of 28GHz and 72GHz with a maximum UE velocity in the small cell of 0.5km/h, coherence times of 77ms and 30ms respectively are given. This tells us that the channel will remain constant over the duration of many slot periods considering Figure 6.8 which may help to minimise the amount of CSI required. If the coherence time is limited the number of orthogonal pilot sequences that are assigned to cell is also limited. This can be a problem when considering LSAS schemes where neighbouring cells use the same carrier frequency and pilot symbols. In such cases pilot contamination can be a problem [160]. Channel Parameter Estimation –Proposed Algorithm Both proposed architectures will require estimation of CSI and significantly estimation of the multipath delay components. In both transmit and receive, data and pilot are separately BPSK modulated on the I and Q channel respectively, orthogonal variable spreading factor (OVSF) codes are suggested on the pilot and data to maintain orthogonality. Different users use different UL scrambling codes to create a unique traffic channel. Neglecting mutual interference between the data and pilots, the signal received at the base station can be separated into I and Q channels. The Q channel can be used to estimate the channel parameters.
Table 6.4 Baseband Power – LTE SBS (10MHz, 64QAM, 4 antennas, ½ rate Turbo) UL/DL Ref (GOPS) Power (W), 65nm, 2010 Power (W), 45nm,2010 UL Filter (inc D/C) 150 2.5 1.25 OFDM 60 1 0.5 FD Lin 30 0.5 0.25 FD non-lin 10 0.167 0.083 FEC 110 1.833 0.917 DL Filter 100 1.667 0.833 OFDM 60 1 0.5 FD lin 20 0.333 0.167 FD non-lin 5 0.333 0.167 FEC 20 0.333 0.167 Total Power (W) 9.667 4.833 Factoring in the transceiver into (6.10) and considering a range of values for b to be between 32 increases the overall power as Table 6.5. The ECR is given based on a theoretical bandwidth of 480Mbits/s. Table 6.5 Total Power – LTE (10MHz, 64QAM, 4 antennas, ½ rate) Turbo) Transceiver power, b, (mw) Total Power (W), 65nm, 2010 ECR J/bit Total Power (W), 45nm, 2012 ECR J/bit 32 9.795 2.04E-8 4.961 1.03E-8 64 9.923 2.07E-8 5.089 1.06E-8 128 10.179 2.12E-8 5.345 1.11E-8 256 10.691 2.23E-8 5.857 1.22E-8 512 11.715 2.44E-8 6.881 1.43E-8 1024 13.763 2.87E-8 8.929 1.86E-8 2048 17.859 3.72E-8 13.025 2.71E-8 4096 26.051 5.42E-8 21.217 4.42E-8 2D-Rake
Because of the simplicity and low complexity theme, the work in thesis is focused towards simple beamforming techniques and predominantly DL beamforming. This is in line with current thinking for future 5G systems where, because of the advent of large scale antenna systems and highly focused beams, more conventional and complex techniques such as spatial multiplexing MIMO are considered less appropriate. In addition, the work has provided some alternative views on the design of 5G SBSs assuming millimetre wave technology is adopted. The arguments for single antenna system suitability and alternative architectures for the physical layer have been provided. These being substantiated by expected performance, complexity and EE. It is inevitable that future SBSs will be equipped with multiple antennas, either to improve SE or to overcome eroded link margin in the case of mmWave. The multiple antennas will be embodied into the SBS. The work in this thesis, therefore provides a suitable reference concerning design approaches for low complexity and anticipated EE. Future Work This thesis has covered a range of access technologies and addressed a range of considerations for the design of SBSs focusing 3rd, 4th and 5th generation wireless. Looking further towards future 5G small cell networks using mmWave, it is evident that there are a number of specific challenges to address to maintain low complex and energy efficient solutions for the SBS; particularly in indoor deployments. Current research in the field advocates the use of large antenna array systems to overcome eroded link margin and because of highly optimised and concentrated beamforming and low multipath capture, expects receiver implementations to be low in complexity and without the need for equalisation and error correction schemes. While this seems a rational assumption, and as has been discussed in this thesis, there are potential issues surrounding this high level approach that need further research and analysis. The following discusses some identified deficiencies in the analysis presented in this thesis that should be considered to further focus the specific case of the indoor SBS for 5G. 5G Dense Deployments – Interference Isolation The work on mmWave has shown that there is potential to have highly isolated small cells. Here, because of the excessive path loss leading to a reduced RF footprint, interference is minimised. In addition, the interference to and from the MBS layer is minimised because of possible path loss through buildings and structures. This should result in the ability to achieve acceptable QoS with a