4,584 research outputs found
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Design guidelines for spatial modulation
A new class of low-complexity, yet energyefficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO) has emerged. These systems are capable of exploiting the spatial dimensions (i.e. the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication whilst relying on a single-Radio Frequency (RF) chain. Moreover, SM may also be viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/ cooperative protocol design issues, and on their meritorious variants
Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
This paper presents a general stochastic model developed for a class of
cooperative wireless relay networks, in which imperfect knowledge of the
channel state information at the destination node is assumed. The framework
incorporates multiple relay nodes operating under general known non-linear
processing functions. When a non-linear relay function is considered, the
likelihood function is generally intractable resulting in the maximum
likelihood and the maximum a posteriori detectors not admitting closed form
solutions. We illustrate our methodology to overcome this intractability under
the example of a popular optimal non-linear relay function choice and
demonstrate how our algorithms are capable of solving the previously
intractable detection problem. Overcoming this intractability involves
development of specialised Bayesian models. We develop three novel algorithms
to perform detection for this Bayesian model, these include a Markov chain
Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary
Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero
Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol
error rate (SER) performance versus signal to noise ratio (SNR) of the three
detection algorithms are studied in simulated examples
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Hybrid Iterative Multiuser Detection for Channel Coded Space Division Multiple Access OFDM Systems
Space division multiple access (SDMA) aided orthogonal frequency division multiplexing (OFDM) systems assisted by efficient multiuser detection (MUD) techniques have recently attracted intensive research interests. The maximum likelihood detection (MLD) arrangement was found to attain the best performance, although this was achieved at the cost of a computational complexity, which increases exponentially both with the number of users and with the number of bits per symbol transmitted by higher order modulation schemes. By contrast, the minimum mean-square error (MMSE) SDMA-MUD exhibits a lower complexity at the cost of a performance loss. Forward error correction (FEC) schemes such as, for example, turbo trellis coded modulation (TTCM), may be efficiently combined with SDMA-OFDM systems for the sake of improving the achievable performance. Genetic algorithm (GA) based multiuser detection techniques have been shown to provide a good performance in MUD-aided code division multiple access (CDMA) systems. In this contribution, a GA-aided MMSE MUD is proposed for employment in a TTCM assisted SDMA-OFDM system, which is capable of achieving a similar performance to that attained by its optimum MLD-aided counterpart at a significantly lower complexity, especially at high user loads. Moreover, when the proposed biased Q-function based mutation (BQM) assisted iterative GA (IGA) MUD is employed, the GA-aided system’s performance can be further improved, for example, by reducing the bit error ratio (BER) measured at 3 dB by about five orders of magnitude in comparison to the TTCM assisted MMSE-SDMA-OFDM benchmarker system, while still maintaining modest complexity
High Capacity CDMA and Collaborative Techniques
The thesis investigates new approaches to increase the user capacity and improve the error
performance of Code Division Multiple Access (CDMA) by employing adaptive interference cancellation
and collaborative spreading and space diversity techniques. Collaborative Coding Multiple
Access (CCMA) is also investigated as a separate technique and combined with CDMA. The
advantages and shortcomings of CDMA and CCMA are analysed and new techniques for both the
uplink and downlink are proposed and evaluated.
Multiple access interference (MAI) problem in the uplink of CDMA is investigated first. The
practical issues of multiuser detection (MUD) techniques are reviewed and a novel blind adaptive
approach to interference cancellation (IC) is proposed. It exploits the constant modulus (CM)
property of digital signals to blindly suppress interference during the despreading process and obtain
amplitude estimation with minimum mean squared error for use in cancellation stages. Two
new blind adaptive receiver designs employing successive and parallel interference cancellation
architectures using the CM algorithm (CMA) referred to as ‘CMA-SIC’ and ‘BA-PIC’, respectively,
are presented. These techniques have shown to offer near single user performance for large
number of users. It is shown to increase the user capacity by approximately two fold compared
with conventional IC receivers. The spectral efficiency analysis of the techniques based on output
signal-to interference-and-noise ratio (SINR) also shows significant gain in data rate. Furthermore,
an effective and low complexity blind adaptive subcarrier combining (BASC) technique using a
simple gradient descent based algorithm is proposed for Multicarrier-CDMA. It suppresses MAI
without any knowledge of channel amplitudes and allows large number of users compared with
equal gain and maximum ratio combining techniques normally used in practice.
New user collaborative schemes are proposed and analysed theoretically and by simulations
in different channel conditions to achieve spatial diversity for uplink of CCMA and CDMA. First,
a simple transmitter diversity and its equivalent user collaborative diversity techniques for CCMA
are designed and analysed. Next, a new user collaborative scheme with successive interference
cancellation for uplink of CDMA referred to as collaborative SIC (C-SIC) is investigated to reduce
MAI and achieve improved diversity. To further improve the performance of C-SIC under high
system loading conditions, Collaborative Blind Adaptive SIC (C-BASIC) scheme is proposed.
It is shown to minimize the residual MAI, leading to improved user capacity and a more robust
system. It is known that collaborative diversity schemes incur loss in throughput due to the need of
orthogonal time/frequency slots for relaying source’s data. To address this problem, finally a novel
near-unity-rate scheme also referred to as bandwidth efficient collaborative diversity (BECD) is proposed and evaluated for CDMA. Under this scheme, pairs of users share a single spreading sequence to exchange and forward their data employing a simple superposition or space-time
encoding methods. At the receiver collaborative joint detection is performed to separate each
paired users’ data. It is shown that the scheme can achieve full diversity gain at no extra bandwidth
as inter-user channel SNR becomes high.
A novel approach of ‘User Collaboration’ is introduced to increase the user capacity of CDMA
for both the downlink and uplink. First, collaborative group spreading technique for the downlink
of overloaded CDMA system is introduced. It allows the sharing of the same single spreading
sequence for more than one user belonging to the same group. This technique is referred to as
Collaborative Spreading CDMA downlink (CS-CDMA-DL). In this technique T-user collaborative
coding is used for each group to form a composite codeword signal of the users and then a
single orthogonal sequence is used for the group. At each user’s receiver, decoding of composite
codeword is carried out to extract the user’s own information while maintaining a high SINR performance.
To improve the bit error performance of CS-CDMA-DL in Rayleigh fading conditions,
Collaborative Space-time Spreading (C-STS) technique is proposed by combining the collaborative
coding multiple access and space-time coding principles. A new scheme for uplink of CDMA
using the ‘User Collaboration’ approach, referred to as CS-CDMA-UL is presented next. When
users’ channels are independent (uncorrelated), significantly higher user capacity can be achieved
by grouping multiple users to share the same spreading sequence and performing MUD on per
group basis followed by a low complexity ML decoding at the receiver. This approach has shown
to support much higher number of users than the available sequences while also maintaining the
low receiver complexity. For improved performance under highly correlated channel conditions,
T-user collaborative coding is also investigated within the CS-CDMA-UL system
Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks
Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level
Dispensing with channel estimation: differentially modulated cooperative wireless communications
As a benefit of bypassing the potentially excessive complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity noncoherent detection constitutes a viable candidate for user-cooperative systems, where estimating all the links by the relays is unrealistic. In order to stimulate further research on differentially modulated cooperative systems, a number of fundamental challenges encountered in their practical implementations are addressed, including the time-variant-channel-induced performance erosion, flexible cooperative protocol designs, resource allocation as well as its high-spectral-efficiency transceiver design. Our investigations demonstrate the quantitative benefits of cooperative wireless networks both from a pure capacity perspective as well as from a practical system design perspective
Collaborative spectrum sensing optimisation algorithms for cognitive radio networks
The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance
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