3,580 research outputs found
Sign-Compute-Resolve for Tree Splitting Random Access
We present a framework for random access that is based on three elements:
physical-layer network coding (PLNC), signature codes and tree splitting. In
presence of a collision, physical-layer network coding enables the receiver to
decode, i.e. compute, the sum of the packets that were transmitted by the
individual users. For each user, the packet consists of the user's signature,
as well as the data that the user wants to communicate. As long as no more than
K users collide, their identities can be recovered from the sum of their
signatures. This framework for creating and transmitting packets can be used as
a fundamental building block in random access algorithms, since it helps to
deal efficiently with the uncertainty of the set of contending terminals. In
this paper we show how to apply the framework in conjunction with a
tree-splitting algorithm, which is required to deal with the case that more
than K users collide. We demonstrate that our approach achieves throughput that
tends to 1 rapidly as K increases. We also present results on net data-rate of
the system, showing the impact of the overheads of the constituent elements of
the proposed protocol. We compare the performance of our scheme with an upper
bound that is obtained under the assumption that the active users are a priori
known. Also, we consider an upper bound on the net data-rate for any PLNC based
strategy in which one linear equation per slot is decoded. We show that already
at modest packet lengths, the net data-rate of our scheme becomes close to the
second upper bound, i.e. the overhead of the contention resolution algorithm
and the signature codes vanishes.Comment: This is an extended version of arXiv:1409.6902. Accepted for
publication in the IEEE Transactions on Information Theor
Non-Linear Self-Interference Cancellation via Tensor Completion
Non-linear self-interference (SI) cancellation constitutes a fundamental
problem in full-duplex communications, which is typically tackled using either
polynomial models or neural networks. In this work, we explore the
applicability of a recently proposed method based on low-rank tensor
completion, called canonical system identification (CSID), to non-linear SI
cancellation. Our results show that CSID is very effective in modeling and
cancelling the non-linear SI signal and can have lower computational complexity
than existing methods, albeit at the cost of increased memory requirements.Comment: To be presented at the 2020 Asilomar Conference for Signals, Systems,
and Computer
Realization of Delayed Least Mean Square Adaptive Algorithm using Verilog HDL for EEG Signals
An efficient architecture for the implementation of delayed least mean square (DLMS) adaptive filter is presented in this paper. It is shown that the proposed architectures reduces the register complexity and also supports the faster convergence. Compared to transpose form, the direct form LMS adaptive filter has fast convergence but both has most similar critical path. Further it is shown that in most of the practical cases, very small adaptation delay is sufficient enough to implement a direct-form LMS adaptive filter where in normal cases a very high sampling rate is required and also it shows that no pipelining approach is necessary. From the above discussed estimations three different architectures of LMS adaptive filter has been designed. They are, first design comprise of zero delays i.e., with no adaptation delays, second design comprises of only single delay i.e., with only one adaptation delay, and lastly the third design comprises of two adaptation delays. Among all the three designs zero adaptation delay structure gives efficient performance comparatively. Design with zero adaptation delay involves the minimum energy per sample (EPS) and also minimum area compared to other two designs. The aim of this thesis is to design an efficient filter structures to create a system-on-chip (SoC) solution by using an optimized code for solving various adaptive filtering problems in the system. In this thesis our main focus is on interference cancellation in electroencephalogram (EEG) applications by using the proposed filter structures. Modern field programmable gate arrays (FPGAs) have the resources that are required to design an effective adaptive filtering structures. The designs are evaluated in terms of design time, area and delays
Systems with Massive Number of Antennas: Distributed Approaches
As 5G is entering maturity, the research interest has shifted towards 6G, and specially the new use cases that the future telecommunication infrastructure needs to support. These new use cases encompass much higher requirements, specifically: higher communication data-rates, larger number of users, higher accuracy in localization, possibility to wirelessly charge devices, among others.The radio access network (RAN) has already gone through an evolution on the path towards 5G. One of the main changes was a large increment of the number of antennas in the base-station. Some of them may even reach 100 elements, in what is commonly referred as Massive MIMO. New proposals for 6G RAN point in the direction of continuing this path of increasing the number of antennas, and locate them throughout a certain area of service. Different technologies have been proposed in this direction, such as: cell-free Massive MIMO, distributed MIMO, and large intelligent surface (LIS). In this thesis we focus on LIS, whose conducted theoretical studies promise the fulfillment of the aforementioned requirements.While the theoretical capabilities of LIS have been conveniently analyzed, little has been done in terms of implementing this type of systems. When the number of antennas grow to hundreds or thousands, there are numerous challenges that need to be solved for a successful implementation. The most critical challenges are the interconnection data-rate and the computational complexity.In the present thesis we introduce the implementation challenges, and show that centralized processing architectures are no longer adequate for this type of systems. We also present different distributed processing architectures and show the benefits of this type of schemes. This work aims at giving a system-design guideline that helps the system designer to make the right decisions when designing these type of systems. For that, we provide algorithms, performance analysis and comparisons, including first order evaluation of the interconnection data-rate, processing latency, memory and energy consumption. These numbers are based on models and available data in the literature. Exact values depend on the selected technology, and will be accurately determined after building and testing these type of systems.The thesis concentrates mostly on the topic of communication, with additional exploration of other areas, such as localization. In case of localization, we benefit from the high spatial resolution of a very-large array that provides very rich channel state information (CSI). A CSI-based fingerprinting via neural network technique is selected for this case with promising results. As the communication and localization services are based on the acquisition of CSI, we foresee a common system architecture capable of supporting both cases. Further work in this direction is recommended, with the possibility of including other applications such as sensing.The obtained results indicate that the implementation of these very-large array systems is feasible, but the challenges are numerous. The proposed solutions provide encouraging results that need to be verified with hardware implementations and real measurements
Coded Slotted ALOHA: A Graph-Based Method for Uncoordinated Multiple Access
In this paper, a random access scheme is introduced which relies on the
combination of packet erasure correcting codes and successive interference
cancellation (SIC). The scheme is named coded slotted ALOHA. A bipartite graph
representation of the SIC process, resembling iterative decoding of generalized
low-density parity-check codes over the erasure channel, is exploited to
optimize the selection probabilities of the component erasure correcting codes
via density evolution analysis. The capacity (in packets per slot) of the
scheme is then analyzed in the context of the collision channel without
feedback. Moreover, a capacity bound is developed and component code
distributions tightly approaching the bound are derived.Comment: The final version to appear in IEEE Trans. Inf. Theory. 18 pages, 10
figure
System Development and VLSI Implementation of High Throughput and Hardware Efficient Polar Code Decoder
Polar code is the first channel code which is provable to achieve the Shannon capacity. Additionally, it has a very good performance in terms of low error floor. All these merits make it a potential candidate for the future standard of wireless communication or storage system. Polar code is received increasing research interest these years. However, the hardware implementation of hardware decoder still has not meet the expectation of practical applications, no matter from neither throughput aspect nor hardware efficient aspect. This dissertation presents several system development approaches and hardware structures for three widely known decoding algorithms. These algorithms are successive cancellation (SC), list successive cancellation (LSC) and belief propagation (BP). All the efforts are in order to maximize the throughput meanwhile minimize the hardware cost.
Throughput centric successive cancellation (TCSC) decoder is proposed for SC decoding. By introducing the concept of constituent code, the decoding latency is significantly reduced with a negligible decoding performance loss. However, the specifically designed computation unites dramatically increase the hardware cost, and how to handle the conventional polar code sets and constituent codes sets makes the hardware implementation more complicated. By exploiting the natural property of conventional SC decoder, datapaths for decoding constituent codes are compatibly built via computation units sharing technique. This approach does not incur additional hardware cost expect some multiplexer logic, but can significantly increase the decoding throughput. Other techniques such as pre-computing and gate-level optimization are used as well in order to further increase the decoding throughput. A specific designed partial sum generator (PSG) is also investigated in this dissertation. This PSG is hardware efficient and timing compatible with proposed TCSC decoder. Additionally, a polar code construction scheme with constituent codes optimization is also presents. This construction scheme aims to reduce the constituent codes based SC decoding latency. Results show that, compared with the state-of-art decoder, TCSC can achieve at least 60% latency reduction for the codes with length n = 1024. By using Nangate FreePDK 45nm process, TCSC decoder can reach throughput up to 5.81 Gbps and 2.01 Gbps for (1024, 870) and (1024, 512) polar code, respectively. Besides, with the proposed construction scheme, the TCSC decoder generally is able to further achieve at least around 20% latency deduction with an negligible gain loss. Overlapped List Successive Cancellation (OLSC) is proposed for LSC decoding as a design approach. LSC decoding has a better performance than LS decoding at the cost of hardware consumption. With such approach, the l (l > 1) instances of successive cancellation (SC) decoder for LSC with list size l can be cut down to only one. This results in a dramatic reduction of the hardware complexity without any decoding performance loss. Meanwhile, approaches to reduce the latency associated with the pipeline scheme are also investigated. Simulation results show that with proposed design approach the hardware efficiency is increased significantly over the recently proposed LSC decoders. Express Journey Belief Propagation (XJBP) is proposed for BP decoding. This idea origins from extending the constituent codes concept from SC to BP decoding. Express journey refers to the datapath of specific constituent codes in the factor graph, which accelerates the belief information propagation speed. The XJBP decoder is able to achieve 40.6% computational complexity reduction with the conventional BP decoding. This enables an energy efficient hardware implementation.
In summary, all the efforts to optimize the polar code decoder are presented in this dissertation, supported by the careful analysis, precise description, extensively numerical simulations, thoughtful discussion and RTL implementation on VLSI design platforms
System Development and VLSI Implementation of High Throughput and Hardware Efficient Polar Code Decoder
Polar code is the first channel code which is provable to achieve the Shannon capacity. Additionally, it has a very good performance in terms of low error floor. All these merits make it a potential candidate for the future standard of wireless communication or storage system. Polar code is received increasing research interest these years. However, the hardware implementation of hardware decoder still has not meet the expectation of practical applications, no matter from neither throughput aspect nor hardware efficient aspect. This dissertation presents several system development approaches and hardware structures for three widely known decoding algorithms. These algorithms are successive cancellation (SC), list successive cancellation (LSC) and belief propagation (BP). All the efforts are in order to maximize the throughput meanwhile minimize the hardware cost.
Throughput centric successive cancellation (TCSC) decoder is proposed for SC decoding. By introducing the concept of constituent code, the decoding latency is significantly reduced with a negligible decoding performance loss. However, the specifically designed computation unites dramatically increase the hardware cost, and how to handle the conventional polar code sets and constituent codes sets makes the hardware implementation more complicated. By exploiting the natural property of conventional SC decoder, datapaths for decoding constituent codes are compatibly built via computation units sharing technique. This approach does not incur additional hardware cost expect some multiplexer logic, but can significantly increase the decoding throughput. Other techniques such as pre-computing and gate-level optimization are used as well in order to further increase the decoding throughput. A specific designed partial sum generator (PSG) is also investigated in this dissertation. This PSG is hardware efficient and timing compatible with proposed TCSC decoder. Additionally, a polar code construction scheme with constituent codes optimization is also presents. This construction scheme aims to reduce the constituent codes based SC decoding latency. Results show that, compared with the state-of-art decoder, TCSC can achieve at least 60% latency reduction for the codes with length n = 1024. By using Nangate FreePDK 45nm process, TCSC decoder can reach throughput up to 5.81 Gbps and 2.01 Gbps for (1024, 870) and (1024, 512) polar code, respectively. Besides, with the proposed construction scheme, the TCSC decoder generally is able to further achieve at least around 20% latency deduction with an negligible gain loss. Overlapped List Successive Cancellation (OLSC) is proposed for LSC decoding as a design approach. LSC decoding has a better performance than LS decoding at the cost of hardware consumption. With such approach, the l (l > 1) instances of successive cancellation (SC) decoder for LSC with list size l can be cut down to only one. This results in a dramatic reduction of the hardware complexity without any decoding performance loss. Meanwhile, approaches to reduce the latency associated with the pipeline scheme are also investigated. Simulation results show that with proposed design approach the hardware efficiency is increased significantly over the recently proposed LSC decoders. Express Journey Belief Propagation (XJBP) is proposed for BP decoding. This idea origins from extending the constituent codes concept from SC to BP decoding. Express journey refers to the datapath of specific constituent codes in the factor graph, which accelerates the belief information propagation speed. The XJBP decoder is able to achieve 40.6% computational complexity reduction with the conventional BP decoding. This enables an energy efficient hardware implementation.
In summary, all the efforts to optimize the polar code decoder are presented in this dissertation, supported by the careful analysis, precise description, extensively numerical simulations, thoughtful discussion and RTL implementation on VLSI design platforms
Modern Random Access for Satellite Communications
The present PhD dissertation focuses on modern random access (RA) techniques.
In the first part an slot- and frame-asynchronous RA scheme adopting replicas,
successive interference cancellation and combining techniques is presented and
its performance analysed. The comparison of both slot-synchronous and
asynchronous RA at higher layer, follows. Next, the optimization procedure, for
slot-synchronous RA with irregular repetitions, is extended to the Rayleigh
block fading channel. Finally, random access with multiple receivers is
considered.Comment: PhD Thesis, 196 page
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