Sequence Sets in Wireless Communication Systems: A Survey

Velazquez-Gutierrez, Juan Manuel; Vargas-Rosales, César

doi:10.1109/comst.2016.2639739

Cited by 45 publications

(21 citation statements)

References 179 publications

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“…For example, the m-sequence set has nearly ideal cyclic auto-correlation property (to be exact, the auto-correlation function of the m-sequence is −1 for any non-zero shift, hence is "nearly" optimal), while its cyclic crosscorrelation and flipped correlation functions are unbounded. The Gold sequence set and the Kasami sequence set (candidate in W-CDMA) have better cyclic cross-correlation properties and acceptable cyclic auto-correlation properties, but their flipped correlations are unbounded (see the excellent survey [21] on the correlation functions of these sequences).…”

Section: A Codes In Legacy Cdma Systemsmentioning

confidence: 99%

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AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

Shao

Liew

Wang

2020

IEEE Trans. Neural Netw. Learning Syst.

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Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This paper puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques. AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game.Each episode ends with a completely-filled sequence set, upon which a reward is given based on the desirability of the sequence set. AlphaSeq models the game as a Markov Decision Process (MDP), and adapts the DRL framework of AlphaGo to solve the MDP. Sequences discovered improve progressively as AlphaSeq, starting as a novice, learns to become an expert game player through many episodes of game playing. Compared with traditional sequence construction by mathematical tools, AlphaSeq is particularly suitable for problems with complex objectives intractable to mathematical analysis.We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multicarrier CDMA systems. 2) AlphaSeq discovers new sequences that triple the signal-to-interference ratio -benchmarked against the well-known Legendre sequence -of a mismatched filter estimator in pulse compression radar systems. Index TermsDeep reinforcement learning, deep neural network, monte-carlo tree search, AlphaGo, multi-carrier CDMA, pulse compression radar.

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Section: A Codes In Legacy Cdma Systemsmentioning

confidence: 99%

“…Specifically, each time we feed a vertex v i into the DNN, it outputs a policy estimation P and a reward estimation R . Each entry in distribution P is exactly the prior probability P (v i , a j ) for each edge of vertex v i , and R will be used for updating the mean reward Q(v i , a j ), given by (21) later.…”

Section: A Mctsmentioning

confidence: 99%

AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

Shao

Liew

Wang

2020

IEEE Trans. Neural Netw. Learning Syst.

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“…2) For u = 1, 2, · · · , U and i = 0, 1, 2, · · · , K, set y u,i equal to L consecutive elements of y ′ that begin at the index that matches the spreading gain vector z u,i . Once y u,i are obtained, the set of decision variables c (u) i,j are calculated, using (12), for each user and compared against the hypothesis distributions H 0 and H 1 . Subsequently, for those users whose decision variables c (u) i,j found to belong to the hypothesis H 1 distribution, the corresponding information bits may be extracted according to (11).…”

Section: Cp-dsss Signal Detectionmentioning

confidence: 99%

Underlay Control Signaling for Ultra-Reliable Low-Latency IoT Communications

Aminjavaheri

RezazadehReyhani

Khalona

et al. 2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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Future mobile networks not only envision enhancing the traditional link quality and data rates of mobile broad band (MBB) links, but also development of new control channels to meet the requirements of delay sensitive use cases. In particular, the need for ultra-reliable low-latency communications (URLLC) for many internet of things (IoT) users is greatly emphasized. In this paper, we present a novel spread spectrum waveform design that we propose for transmission of control signals to establish URLLC communications. These control signals are transmitted over the spectral resources that belong to the MBB communications in the network, but at a level that minimally affects these data channels. The proposed waveform, although a direct sequence spread spectrum (DSSS) technique, is designed to take advantage of symbol synchronization available to the OFDM broad band communications in the network. This allows simple synchronization with the rest of the network. The proposed DSSS method can transmit single and multiple bits within each OFDM time frame and can serve many user equipment (UE) nodes simultaneously.

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“…In fact, the former is affected by the cross-correlation properties between different codes of the sets whereas the latter is influenced by the autocorrelation properties, that is the correlation between timeshifted versions of the same code [16]- [20]. In wireless communication systems, orthogonal sequences have been used to achieve communication among users and simultaneously manage interference as well as multiple access [21]- [23]. In this respect, structured binary sequences, like Legendre, Gold, Kasami, m-sequences, Gong, Paterson and Weil have been introduced and used in many CDMA/wireless communication systems [24]- [32].…”

Section: Introductionmentioning

confidence: 99%

Designing Sets of Binary Sequences for MIMO Radar Systems

Alaee-Kerahroodi

Modarres-Hashemi

Naghsh

2019

IEEE Trans. Signal Process.

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In this paper, we aim at designing sets of binary sequences with good aperiodic/periodic auto-and cross-correlation functions for Multiple-Input-Multiple-Output (MIMO) radar systems. We show such a set of sequences can be obtained by minimizing a weighted sum of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL) with the binary element constraint at the design stage. The sets of designed sequences are neighboring the lower bound on ISL and have a better PSL than the best known structured sets of binary sequences. To formulate the problem, we introduce a Pareto-objective of weighted auto-and cross-correlation functions by establishing a multi-objective NP-hard constrained optimization problem. Then, by using the block coordinate descent (BCD) framework, we propose an efficient monotonic algorithm based on Fast Fourier Transform (FFT), to minimize the multi-dimensional objective function. Numerical results illustrate the superior performance of the proposed algorithm in comparison with the state-of-the-art methods.

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Sequence Sets in Wireless Communication Systems: A Survey

Cited by 45 publications

References 179 publications

AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

AlphaSeq: Sequence Discovery With Deep Reinforcement Learning

Underlay Control Signaling for Ultra-Reliable Low-Latency IoT Communications

Designing Sets of Binary Sequences for MIMO Radar Systems

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