Hardware implementation and on-board prototyping 

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Mobile broadband service and OFDM in 4G

MBB communications scenario refers to the traditional mobile Internet access service. It became available for the first time in 1991 as part of the 2nd Generation mobile networks (2G). Higher speeds have then been specified in 2001 and 2006 as part of the third (3G) and fourth (4G) generations. In 5G, this scenario is referred to as enhanced Mobile BroadBand (eMBB). It extends the support of conventional MBB through improved data rates, capacity, and coverage. Typical use cases are multimedia streaming, voice-over-IP, internet browsing, videoconferencing, file downloads, etc.
OFDM, with data transmitted on a large number of parallel narrow-band subcar-riers, is the core of the 4G/LTE downlink radio transmission. Due to the use of rela-tively narrowband subcarriers in combination with a cyclic prefix, OFDM transmission is inherently robust to time dispersion on the radio channel without a requirement to resort to advanced and potentially complex receiver-side channel equalization. For the uplink, where the available transmission power is significantly lower than for the down-link, single-carrier transmission, based on Discrete Fourier transform (DFT)-precoded OFDM, referred to as Single-Carrier Orthogonal Frequency-Division Multiplexing (SC-OFDM), is used. SC-FDMA has a smaller peak-to-average power ratio than regular OFDM, thus enabling less complex and/or higher-power terminals. OFDM modulation: Originally proposed by Weinstein and Ebert in 1971 [7], OFDM has been widely used in wireless communication systems [2], such as Digital Video Broad-casting – Terrestrial standard and IEEE 802.11 standard (Wi-fi), in addition to 4G/LTE standard. OFDM is a multi-carrier modulation that divides the available bandwidth into multiple elementary narrow-band signals, called subcarriers.
These subcarriers are modulated by complex valued symbols issued from a conven-tional digital modulation scheme (such as Quadrature Amplitude Modulation (QAM)), carrying the data to transmit. In frequency domain, each transmitted subcarrier results in a cardinal sine function spectrum with side lobes that produce overlapping spectra between subcarriers The subcarriers are equally spaced in frequency domain, so that the individual peaks of subcarriers all line up with the nulls of the other subcarriers as illustrated in Figure 1.1, where subcarrier frequency spacing is denoted by ∆F . The resulting orthogonality allows to receive the symbols without interference in both time and frequency.

Massive machine communications

Massive machine communications can be considered as one part of Machine Type Com-munications (MTC) [11]. The respective 3rd Generation Partnership Project (3GPP) term is massive MTC (mMTC). This scenario corresponds to the case where a massive amount of actors and/or sensors are deployed anywhere in the landscape and that need to access the wireless network. Today, this is also known as Internet-of-Things (IoT).
Typical use cases are smart metering, natural ecosystem monitoring, remote mainte-nance/control, flock/fleet tracking/tracing, remote diagnostics etc. A common assump-tion is that the communication in MMC is mostly unidirectional: uplink is the dominant communication [12] [13]. Furthermore, the transmission is sporadic and typically involves relatively small packets per connection. Consequently, the required data rate is typically low, with around 1 kb/s to 10 kb/s for each device. On the other hand, the user den-sity is much higher than what is generally assumed for MBB services. In this regard, it is estimated that up to 300000 devices must be able to communicate inside a single cell [12]. Such user density is clearly not supported in the current 4G/LTE systems. Finally, one of the most important aspects of MMC is that the sensors have generally limited available hardware resources and should operate for long battery life. Therefore, the complexity and the energy consumption related to the hardware implementation of the modulation techniques must be kept as low as possible.
In order to reduce the energy-consumption in this scenario which implies a huge number of devices, the signalling overhead introduced by the synchronization procedure must be minimized. Therefore, the ideal transmission scheme for MMC should be as follows:
1. the sensor/UE wakes-up from sleep mode,
2. it transmits the data to the BS,
3. it returns again in sleep mode.
This is in practice difficult to achieve since it requires the support of asynchronous communications. In this case, there is no prior synchronization with the BS. From the physical layer point of view, this means that the relative delays between the received user signals are random and can be higher than the symbol duration. As the OFDM modula-tion assumes strict orthogonality between users, such scenario of communications cannot be supported by OFDM. An alternative solution is to synchronize each sensor in time with the BS. This time synchronization can be obtained, for instance, through the down-link reference signals transmitted by the BS [14]. However, since the users are localized at different distances from the BS, each one can have different propagation delay. There-fore, at the BS, the received signals sharing the same time base overlap. Consequently, a time misalignment is created which may introduce undesirable interference across all users. An example is illustrated in Figure 1.2. Two devices, identified as UE1 and UE2, transmit an OFDM signal through their corresponding channel respectively having a propagation delay of τ1 and τ2. Thanks to the use of a CP, the received data of each device can be perfectly recovered if the timing offset between devices ∆τ is lower than the CP duration TC P , i.e. when TC P < |τ1 − τ2|. This requires to apply a circular shift operation on the OFDM symbol for each device. Such operation is efficiently implemented after the FFT, in frequency domain, thanks to a simple linear phase rotation operation. However, if ∆τ > TC P then for some devices the receiver processing window at the BS will not be aligned with the OFDM symbol (including CP) to demodulate. Part of the next (or previous) OFDM symbol is therefore included in the processing window.

READ  Reduction algorithm for energy savings in wireless sensor networks 

Vehicular-to-anything communications

V2X service corresponds to communications between two vehicles, referred to as V2V, or between a vehicle and the infrastructure, referred to as V2I. Today, also known as Intelligent Transportation Systems [1], V2X consists of use cases where other core services involve nodes with high speeds. In addition to the support of high speeds, the related technical requirements are often combined to those encountered in the other 5G services. For instance, collision avoidance systems may require low latency with high reliability, and can be included in both MCC and V2X services. Other examples are pay-as-you-drive which is the combination of V2X with MMC, and infotainment which can correspond to the combination of V2X with MBB. Such combination of more than one core service may also be called as composite services [1]. Figure 1.4 provides an example of a wireless environment involving V2X service.
The cellular V2I service is closely related to the MBB service, where users com-municate through a BS.In the scenario foreseen in 5G, the user is located in a vehicle moving at speed ranging from 70 km/h for a car in an urban environment, to more than 500 km/h for high-speed railway [21]. Such high speed is theoretically not supported by the current 4G/LTE standard, where the mobility speed limit is considered to be 300 km/h [22]. In 5G, this speed limit has been increased up to 500 km/h [22].

Table of contents :

Résumé long
Introduction
1 5G and post-OFDM waveform candidates 
1.1 OFDM and considered 5G scenarios
1.1.1 Mobile broadband service and OFDM in 4G
1.1.2 Massive machine communications
1.1.3 Mission critical communications
1.1.4 Vehicular-to-anything communications
1.2 Post-OFDM waveforms
1.2.1 FBMC/OQAM waveform
1.2.2 UF-OFDM waveform
1.2.3 Other 5G waveform candidates
1.3 Motivation for using short PF with FBMC
1.3.1 Support of short frame sizes for low-latency communication
1.3.2 Low computational complexity
1.3.3 High energy efficiency
1.3.4 Efficient block-based channel estimation
1.3.5 Limitations of short PFs
1.4 Summary
2 Novel short prototype filter for FBMC 
2.1 FBMC/OQAM transceivers
2.1.1 PolyPhase Network FBMC transceiver
2.1.2 Frequency-Spread FBMC receiver
2.2 Design of a novel short prototype filter
2.2.1 MMB4 long prototype filter
2.2.2 TFL1 and QMF1 short prototype filters
2.2.3 Proposed NPR1 short prototype filter
2.3 Performance evaluation and comparisons
2.3.1 Comparison of out-of-band power leakage
2.3.2 Truncation impact on the frequency response of the filter
2.3.3 Robustness against timing offset
2.3.4 Robustness against frequency offset
2.3.5 Performance comparison over multipath channels
2.4 Summary
3 Novel FBMC/OQAM receiver techniques for short filters 
3.1 Overlap-Save FBMC receiver technique
3.1.1 FBMC/OQAM equalization techniques
3.1.2 Time-domain equalizer based on Overlap-Save technique for FBMC
3.1.3 Proposed Overlap-Save FBMC receiver
3.2 Complexity reduction of the OS-FBMC receiver
3.2.1 Complexity reduction of the filtering stage
3.2.2 Impact of the truncation on the receiver performance
3.2.3 Overlap-Save-Block FBMC receiver
3.3 Performance evaluation and comparisons
3.3.1 Frame length, data rate and latency
3.3.2 Robustness against timing offset
3.3.2.1 OS-FBMC receivers
3.3.2.2 OSB-FBMC receivers
3.3.2.3 FS-FBMC receivers
3.3.3 Performance comparison over multipath channels
3.3.4 Computational complexity comparison
3.3.5 Strengths and weaknesses of the FBMC transceivers
3.3.6 Support of MIMO Alamouti
3.3.7 Final discussions
3.4 Summary
4 Low-complexity transmitter for UF-OFDM 
4.1 Existing UF-OFDM transmitters
4.1.1 Baseline UF-OFDM transmitter
4.1.2 Frequency domain approximation UF-OFDM transmitter
4.1.3 Time domain windowed UF-OFDM transmitter
4.2 Proposed low-complexity UF-OFDM transmitter
4.2.1 Description of the proposed technique
4.2.2 Adaptation for any subband size
4.2.3 Flexibility to support OFDM modulation
4.3 Computational complexity analysis and comparisons
4.3.1 Complexity analysis
4.3.1.1 FDA UF-OFDM
4.3.1.2 TDW UF-OFDM
4.3.1.3 Proposed UF-OFDM
4.3.2 Complexity comparison
4.4 Summary
5 Hardware implementation and on-board prototyping 
5.1 Hardware architecture of OFDM and UF-OFDM
5.1.1 OFDM transmitter
5.1.2 UF-OFDM transmitter
5.1.3 OFDM and UF-OFDM receivers
5.2 Hardware architecture of FBMC/OQAM transmitter
5.2.1 FBMC transmitter based on pruned IFFT algorithm
5.2.2 Hardware architecture
5.2.2.1 OQAM mapper
5.2.2.2 Pre-processing unit
5.2.2.3 Reorder unit
5.2.2.4 PolyPhase Network unit
5.2.3 Implementation results
5.2.3.1 Analytical hardware complexity comparison
5.2.3.2 Synthesis results on FPGA
5.3 Hardware architecture of the FBMC/OQAM receiver
5.3.0.1 Complexity reduction of the FS filtering stage
5.3.1 Proposed hardware architecture for the FS filtering stage
5.3.2 Hardware complexity comparison
5.4 5G platform for on-board prototyping
5.4.1 Platform description
5.4.2 Demonstration results
5.4.2.1 MMC scenario
5.4.2.2 MCC scenario
5.4.2.3 V2X scenario
5.4.3 Hardware complexity comparison
5.5 Summary
Conclusions and perspectives

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